{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "65a3b8d1-10ff-4b71-a5f3-d4752f3e62d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#利用torchvision载入训练数据MINST\n",
    "#MINST是一个小型的基于灰度图像(图像大小1x28x28)的手写字母识别数据集，包含60000个训练数据，10000个测试数据。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2b57d115-afdd-4150-8395-1ba6ba4d1165",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image size is (28, 28)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from sklearn import svm\n",
    "from sklearn import tree\n",
    "from sklearn.linear_model import LogisticRegression, LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, mean_absolute_error\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "import seaborn as sns\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.multiclass import OneVsOneClassifier\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "import time\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "color_list = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan']\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "label_size = 18 # Label size\n",
    "ticklabel_size = 14 # Tick label size\n",
    "\n",
    "# Load the MNIST dataset to display\n",
    "imgDisp = torchvision.datasets.MNIST(root='./data', train=False, download=True)\n",
    "img, label = imgDisp[0]\n",
    "\n",
    "print(f'Image size is {img.size}')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7,7))\n",
    "ax.imshow(img, cmap='gray') # Display image\n",
    "ax.tick_params(axis='both', which='major', labelsize=ticklabel_size) # Set tick label size\n",
    "ax.set_title(f\"Label: {label}\", fontsize=label_size)\n",
    "# plt.savefig(f'exp_character{label}.png', dpi=300) # Make figure clearer\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "857c638b-3033-4b90-bb32-8fe04e62fc56",
   "metadata": {},
   "outputs": [],
   "source": [
    "#构建ftrExtract类，在读取数据的时候提取样本的特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "af7f14a6-1ec3-4d4e-ad82-469f3974253c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class 0: Trainset - 5923 (9.87%), Testset - 980 (9.80%)\n",
      "Class 1: Trainset - 6742 (11.24%), Testset - 1135 (11.35%)\n",
      "Class 2: Trainset - 5958 (9.93%), Testset - 1032 (10.32%)\n",
      "Class 3: Trainset - 6131 (10.22%), Testset - 1010 (10.10%)\n",
      "Class 4: Trainset - 5842 (9.74%), Testset - 982 (9.82%)\n",
      "Class 5: Trainset - 5421 (9.04%), Testset - 892 (8.92%)\n",
      "Class 6: Trainset - 5918 (9.86%), Testset - 958 (9.58%)\n",
      "Class 7: Trainset - 6265 (10.44%), Testset - 1028 (10.28%)\n",
      "Class 8: Trainset - 5851 (9.75%), Testset - 974 (9.74%)\n",
      "Class 9: Trainset - 5949 (9.92%), Testset - 1009 (10.09%)\n",
      "Input_size is 112\n"
     ]
    }
   ],
   "source": [
    "class ftrExtract(object):\n",
    "    '''\n",
    "    This class is used to extract features of images\n",
    "    '''\n",
    "    def __call__(self, tensor):\n",
    "        tensor = tensor.squeeze() # Compress redundant demensions\n",
    "\n",
    "        mean_width = tensor.mean(axis=0)\n",
    "        mean_height = tensor.mean(axis=1)\n",
    "\n",
    "        std_width = tensor.std(axis=0)\n",
    "        std_height = tensor.std(axis=1)\n",
    "\n",
    "        ftrs = torch.cat([mean_width, mean_height, std_width, std_height])\n",
    "\n",
    "        return ftrs\n",
    "\n",
    "# Define a transform to normalize the data\n",
    "transform = transforms.Compose([transforms.ToTensor(), ftrExtract()])\n",
    "\n",
    "# Load the MNIST dataset\n",
    "trainset = torchvision.datasets.MNIST(root='./Data', train=True, download=True, transform=transform)\n",
    "testset = torchvision.datasets.MNIST(root='./Data', train=False, download=True, transform=transform)\n",
    "\n",
    "# Count number of each class in trainset\n",
    "train_class_counts = {}\n",
    "for _, label in trainset:\n",
    "    if label not in train_class_counts:\n",
    "        train_class_counts[label] = 0\n",
    "    train_class_counts[label] += 1\n",
    "\n",
    "# Count number of each class in testset\n",
    "test_class_counts = {}\n",
    "for _, label in testset:\n",
    "    if label not in test_class_counts:\n",
    "        test_class_counts[label] = 0\n",
    "    test_class_counts[label] += 1\n",
    "\n",
    "# Print results\n",
    "for i in range(10):\n",
    "    cls_counts_train = train_class_counts.get(i, 0)\n",
    "    cls_ratio_train = cls_counts_train / len(trainset)\n",
    "    cls_counts_test = test_class_counts.get(i, 0)\n",
    "    cls_ratio_test = cls_counts_test / len(testset)\n",
    "\n",
    "    print(f\"Class {i}: Trainset - {cls_counts_train} ({cls_ratio_train:.2%}), Testset - {cls_counts_test} ({cls_ratio_test:.2%})\")\n",
    "\n",
    "batch_size = 42\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=0)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=0)\n",
    "\n",
    "# Get a batch of training data\n",
    "dataiter = iter(trainloader)\n",
    "data, labels = next(dataiter)\n",
    "\n",
    "input_size = data[0].numpy().shape[0]\n",
    "print(f'Input_size is {input_size}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4a85a7af-aa6d-4c1d-9f84-1f62c09c1e00",
   "metadata": {},
   "outputs": [],
   "source": [
    "#使用线性回归识别手写字母"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "17f6f86b-201d-444a-aa24-9e40da1cec43",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapes of X_train and Y_train: (60000, 112) and (60000,)\n",
      "Shapes of X_test and y_test: (10000, 112) and (10000,)\n"
     ]
    }
   ],
   "source": [
    "# Convert data to numpy arrays\n",
    "X_train = []\n",
    "y_train = []\n",
    "for batch_image, batch_label in trainloader:\n",
    "    X_train.append(batch_image.view(-1, input_size).numpy())\n",
    "    y_train.append(batch_label.numpy())\n",
    "\n",
    "X_train = np.vstack(X_train)\n",
    "y_train = np.concatenate(y_train)\n",
    "\n",
    "print(f'Shapes of X_train and Y_train: {X_train.shape} and {y_train.shape}')\n",
    "\n",
    "X_test = []\n",
    "y_test = []\n",
    "for batch_image, batch_label in testloader:\n",
    "    X_test.append(batch_image.view(-1, input_size).numpy())\n",
    "    y_test.append(batch_label.numpy())\n",
    "\n",
    "X_test = np.vstack(X_test)\n",
    "y_test = np.concatenate(y_test)\n",
    "\n",
    "print(f'Shapes of X_test and y_test: {X_test.shape} and {y_test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "650a1ffc-c92c-411f-b6c1-aa5f2803f77c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#使用回归的方法做分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "20978e10-0082-443a-8a65-563031b6fd8f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted classes: [0 1 2 3 4 5 6 7 8 9]\n",
      "Real classes: [0 1 2 3 4 5 6 7 8 9]\n",
      "Accuracy of linear regression model: 0.2436\n"
     ]
    }
   ],
   "source": [
    "# Initialize the linear regression model\n",
    "lr_model = LinearRegression()\n",
    "\n",
    "# Train the model\n",
    "lr_model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = lr_model.predict(X_test)\n",
    "\n",
    "# Round predictions to nearest integer for classification\n",
    "y_pred_constrained = np.clip(y_pred, 0, np.max(y_test))\n",
    "y_pred_rounded = np.round(y_pred_constrained).astype(int)\n",
    "print(f\"Predicted classes: {np.unique(y_pred_rounded)}\")\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = np.mean(y_pred_rounded == y_test)\n",
    "print(f\"Real classes: {np.unique(y_test)}\")\n",
    "print(f\"Accuracy of linear regression model: {accuracy:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e96b0e72-f874-41a1-9ab8-cbb6abfde87a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#用混淆矩阵展示线性回归分类的结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "221b26dc-1842-4cb2-87db-926049ee725f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Real label 0, Predict label 0 (168), Predict label 1 (284), Predict label 2 (300), Predict label 3 (164), Predict label 4 (46), Predict label 5 (12), Predict label 6 (6), Predict label 7 (0), Predict label 8 (0), Predict label 9 (0)\n",
      "Real label 1, Predict label 0 (40), Predict label 1 (332), Predict label 2 (389), Predict label 3 (278), Predict label 4 (78), Predict label 5 (14), Predict label 6 (3), Predict label 7 (0), Predict label 8 (0), Predict label 9 (1)\n",
      "Real label 2, Predict label 0 (20), Predict label 1 (85), Predict label 2 (230), Predict label 3 (366), Predict label 4 (233), Predict label 5 (76), Predict label 6 (18), Predict label 7 (4), Predict label 8 (0), Predict label 9 (0)\n",
      "Real label 3, Predict label 0 (0), Predict label 1 (10), Predict label 2 (90), Predict label 3 (264), Predict label 4 (344), Predict label 5 (198), Predict label 6 (82), Predict label 7 (15), Predict label 8 (6), Predict label 9 (1)\n",
      "Real label 4, Predict label 0 (3), Predict label 1 (3), Predict label 2 (24), Predict label 3 (119), Predict label 4 (288), Predict label 5 (248), Predict label 6 (116), Predict label 7 (96), Predict label 8 (63), Predict label 9 (22)\n",
      "Real label 5, Predict label 0 (0), Predict label 1 (5), Predict label 2 (26), Predict label 3 (168), Predict label 4 (295), Predict label 5 (236), Predict label 6 (104), Predict label 7 (46), Predict label 8 (12), Predict label 9 (0)\n",
      "Real label 6, Predict label 0 (0), Predict label 1 (9), Predict label 2 (24), Predict label 3 (83), Predict label 4 (154), Predict label 5 (282), Predict label 6 (251), Predict label 7 (113), Predict label 8 (35), Predict label 9 (7)\n",
      "Real label 7, Predict label 0 (0), Predict label 1 (0), Predict label 2 (4), Predict label 3 (14), Predict label 4 (25), Predict label 5 (57), Predict label 6 (222), Predict label 7 (448), Predict label 8 (224), Predict label 9 (34)\n",
      "Real label 8, Predict label 0 (0), Predict label 1 (1), Predict label 2 (7), Predict label 3 (61), Predict label 4 (218), Predict label 5 (287), Predict label 6 (234), Predict label 7 (107), Predict label 8 (42), Predict label 9 (17)\n",
      "Real label 9, Predict label 0 (1), Predict label 1 (1), Predict label 2 (4), Predict label 3 (12), Predict label 4 (43), Predict label 5 (52), Predict label 6 (58), Predict label 7 (270), Predict label 8 (391), Predict label 9 (177)\n"
     ]
    }
   ],
   "source": [
    "def manual_confusion_matrix(y_true, y_pred, num_classes):\n",
    "    cm = np.zeros((num_classes, num_classes), dtype=int)\n",
    "    for t, p in zip(y_true, y_pred):\n",
    "        cm[t][p] += 1\n",
    "    return cm\n",
    "\n",
    "# Get number of character types\n",
    "num_classes = len(np.unique(y_test)) \n",
    "\n",
    "# Calculate confusion matrix\n",
    "cm = manual_confusion_matrix(y_test, y_pred_rounded, num_classes)\n",
    "\n",
    "# Print the results in the specified format\n",
    "for i in range(num_classes):\n",
    "    output = f\"Real label {i}, \"\n",
    "    for j in range(num_classes):\n",
    "        output += f\"Predict label {j} ({cm[i, j]}), \"\n",
    "    print(output[:-2])  # Remove the last comma and space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "36e74c35-42e8-49f0-bb17-9ce4185c9dbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#画混淆矩阵图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f5e2b8a4-307b-452c-b898-31a10e803a0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KvFTHEYC9Nx4SmZCutr8Pu1YlOTOHX08WPfixvDM30mN8Ww/GtfHg5UaVsDUz4IfDwYXGL5gZqnL8lMyiZ1R61C3pUblH/11c+ZQiyldUSfGqLnthAdfx3buNcTO/YfHhq3y9/RTt+w8jLOA6P0+fWGgbpVLJirkfYGpuyeC3P9ZG2KXm5+8Wqror/fhrwVPy4sRGRwNw/epltv2+kW9++o2rwfc4dfkWw0a/xvWrfkx8tWLM/FEWVq34jYP799G+Qye69+il7XBKXfTDhwD8+P23WFhYcPzUWe7HJrH/8HGqVqvOj99/y7Jfl2g5Su1JSkwEwNLSssj15hYWJOaXedFI3RTveambCnH3EBioysKK6pb0KFN7VKY8ezQ2PS83lwETphUMqDa1sGTMJwuIDLrJHX8/Av3OU61BU0A1Teuvn07mjr8f4+f8QO3mbQEFNy+cYuM3s7nme4wZK7bhUPnZfXm1TQF81LUqDSpbsvPafQ7eii60voaDGfP61iQ0No3xG/wIj0/H2kSf/vWcmdzek3ouFszcdaugfL96TjR2s+LDbf6kZxd9o1ze3UvMoNn8Y+gowNHCiK7eDrzZ3pN6lS34ZOuNEh3Q/DxS5qm6ceXl5vLyG+8VDKg2tbDitRkLiQy6RfD1y9z2O0f1Bs0AOPLHOm6cP8V7P6zF2NRMa7GXtBvXr/LTN18y4a0p1K3f8Jnl85SqusvNzeW9T2YxaPhoAKysrFn43WJu+V/n8sVznDtzimYtWpdq7OXdvr27eX/qO7i5ubNs5Vpth1Mm8vK/WwYGBmz6fRvOlSoB0LpNW9Zv+p0Wjevz0/ffMv6NN5+2GyHEc6hCtDwkPiNTs3hGppaZmUlSUlKhf9rw5MxODdqpN3vXb9sFgNCbVwuW7VmzGD+fQ4z5eAHNu/XFzNIaM0srmnZ+iVc/W0RKQhw7V/xQ+sGXgPe7VKVbLQcO3HzIt4eDC63T1VEws1d1UMKnO28RGJ1KZk4e95MyWXoylCMB0bSrakuDyqpzwM7UgDfaeLDX/wHnwhK0cDQlK0+pSiTW+oaz9HgIHWvY07+hqqtNSqZqnIeZYdEDX03zlz8q9+i/iytvVkT5iurJ71TDIr5TDfK/UyE3VN+puIf32fLzAtr0HkS9IiYhqMimvTUOd48qTP3oM43Km1s8/j3t2uMltfVduquerl/1u1QyAVZQhw7uZ9SwQTg4OLJz3yGcnJ2fvdFz4NH1tmHjJgWJwyPe3rXx9KzCnTvBJCQkaCE67bPIr5/i7j2S82d1fBFJ3RTveambCtHy8F8tWLCAzz//XNth4FDZHR1dXfJyczEu4sVuj172lpWZUbDs6qkjANRo3FKtfM3GLVEoFITeul5KEZcMBarZkHrVduTQrWi+PBCoNp2+m7UxLlbGHA+MITNHfdDzpYhEOtWwp4aDKX6RiVS2NsLEQJeetR3pWduxyM89NkX1tLT3kjPFduEpj86GxAFeNHKz4s9Ld58Y+2BSZHm3/OURcY+7dUXEpVOvsmXBy+Se5GqjXr6icnT1KPhOmZir/+D+/Tv1ICKEjLRUTu76nZO7fi9yn2OaqmYdWnzkGqZF7LO8unFdlSBVq1R0zP17qLo4Llu7he4v9cXD0wtdXV1yc3OxtLRSK2+RvywjveKfJ//WwQP7GDFkILa2duzafxhPzyraDqnMVKteAwCrIs4NAEsr1fKM9HSwKrrM8+xRn/WgoEAaNW5caF18fDwxMTG0aNlKG6FpndRN8Z6XuqkQyYPlMzK1pGdkah9//DHTpk0rVN5VCy+20TcwxKtuYwL9znEvJLCga9Ijd0NUXa+enKIyJ0f1XoGUhDi1LhYpCfEolUr09Q1KOfJ/78nE4UhANPP33y40zuERfV3VfN1WxkX30360PCt/lqbY1Gx2X39QZNmO1e0w1NNh3w1Vn92snIrV9cfOzBCA3PyKCo9L52FyJvUqW2Ckr1NoxiUDXR0auFnyMDmzIMkAuBSeQL3KljT3tGbP3+qpRRXrgjIVnb6BIVXrNeb25XPcDbld0DXpkYLvVCXV993K1kFt1rJHzh3cSVZmRkF3Qn19w1KMvOQNHTm2yOVnfU8SEhxE1x69sbWzo7KbqoujoaEhjZu14JzvKW4H3FTrmhQYcBMAV7fy3yWyNBw8sI/hg1/G2tqGXfsP4+X1Ys0u1K69airwgFs31dZlZ2dzJzgIU1NT7Oztyzq0cqFtu/YsWriAwwcPMHhI4d+UQwcPFJR5EUndFO95qZsKkTw8GusQGBhI42IytVatis/UDA0NMTQsHzcCHV8eQaDfObYv/553v12JvoEqrnuhQZza/QdGJmbUadmhoHzVeo2JCg5gx/LvefXTRejkz7Wdl5fHX799CxTdKlEeKFANaO5Z25Gjt2P4Yl/RiQOopmlNycyhTiULmrhZceGJG1tbUwP61XcCwC9SlUBGxKez6FBQkftq7GaJjYlBsevLg2oOZtxNTC/0DgYACyM9JnXwBOB08OMX5W33u1cwsPrJl8SNaeWGpbF+oQHWALuu3mdkc1debe3O8cCYQi+J61XHiYi4dC6EJpTOwZWxTgNHcfvyObb99h3Tvl9d8J26GxqEz67fMTI1o15L1Y+xs4cXr3/6VZH7uXHuJImx0cWuL++++mFpkcunvTWOkOAg3prygdpL4ka9OoFzvqf4buE8Vm/6q+B3Muh2AL9vWoeZmTntO6tPVPG8e5Q4WFlbs3v/4SJnRnneVfHyonOXbhw+dIDVK5cz9rVxBeu+WfQlCQkJDB0+8oWYtrYoHTt1xrNKFTZv+h+TJr9D/QYNANXLvr6cPxc9PT1GjR6r1Ri1ReqmeM9L3VSIb3379u1ZsGABBw4cYOjQwpnagQMHCspoy4ntmwi8ch6AqCDVdHY+2zcTcOkMAA3bd6NR++4ANOvWl4vH9nPxyB5mj+xJ7ebtSE9N5uLRfWRnZTJu1reYPtEXuferk/HzOcTpPVsJvXWdWk1USVLApTNEBt3CzrkyvUYXnk2mvBjTwpWetR1Jy8olMj6dUc3VW3tOBscRFJ1Kdq6SxSdC+LBrNRb29+ZMSDxh8WnYmOjTxssWM0M9tvndIyS26OlKK5re9Zzo18CZi2Hx3EvMJCM7FydLI1p72WBqqMfhW9Hs93/cYrDuTDjtqtkyuqUb1R3NuHU/mWoOZrSuakvA/WTWnSn8crzwuHSW+YTyZocq/G9cU47cisZYX5du3g7o6SqYvzegXA/GPvbXRgL9VN+piGDVIPnj2zdy66LqreGNOnSncQfVd6pFt75cPLqX84f38NnwHtRp0Y70lGQuHN1LdlYmE2Z/h6mFlVaOo7zr+/Jg9u7azp4dW+nRrintOnUlOSmRvTv/IjMjg+8Wr8DKylrbYZaINauW43v6FAA3/K/nL1tZMKd67z796N23P7cDbjF88MtkZmbSpm17ft+ySW1f7u7uam+mfh599+MvdO7QmslvTmDXju1Ur1GDK35+HD92BDd3d75YUDET7ZKgp6fHkl+X06dXd7p0bMvgIcMwt7Bg+19bCQ0JYfaceVSrXl3bYWqF1E3xnpe6USiV5fgOIl9OTg41atQgKiqKM2fO0OCJTK1ly5YEBATg7+9PdQ0r/FE3p58PXys04PLfWjHnPU7v/rPY9X3HvUu/8VML/s7NyeHw76vx2bGFh5Gh6OsbUqVOA14a+xY1GqnPOZ8Y+5Ddqxdz7fQx4h7cBQXYOlWmfptOvDT2LcwsS/bivvZ0ZInsZ3q3qvTwLno8wiNfHggs6F4E0MjVklcaVqKWkxkWRvqkZ+cSHJ3Kbv8HHLgZ/ZQ9PbbptcbYmBjQ7Wff/xR/UdLSsktkP/UrW9K3vhN1XCywNzPESF+HxIwcAu4ns+faAw48USePmBrqMr6NB51q2mNrZkBsShZHbkWz7GSoWgvGI91rOzCsaWWq2JuSnavkWlQiv54I5ea95BI5jidN7uZVYvtaNnsaJ3f/Uez6/uOnMGDC466IuTk5HNyymhPbN/EwMgw9fQO86jakz9jJGr/H4b2+rUiMjWb5qdKZua1jFYdS2e+zTHtrHH9sWs9f+46rtTyA6vd19bLFbFq/mrCQYAwMDGnYpBmTp35Ii9btyiRGK5OnTytbEiaOf5X/rS9+pqTpM2byyaez8DlxjJe6d37qvtq0bc+eA0dKOsQi6egoyuRzihMZEcG8ObM4eGAfcbGxODo50eulPkyfMRMHB+2c04/oarluAM6fO8e8ObM4e8aXrKwsannXZvI7Uxg2fIS2Q9M6qZvilce6SUpKwtHWksTERCws1MflPqlCJA8AR48epXv37hgaGjJs2DAsLCzYunUrISEhzJs3jxkzZmi8r5JOHp43JZU8PI9KKnl4HpVk8vA80lbyUBGURfJQUWk7eSjPykPyIMTz4p8kDxWi2xJAx44dOXnyJLNmzWLLli1kZWVRu3Zt5s6dy4gRksUKIYQQQghR2ipM8gDQrFkz9u7dq+0whBBCCCGEeCFViJfECSGEEEIIIbRPkgchhBBCCCGERiR5EEIIIYQQQmhEkgchhBBCCCGERiR5EEIIIYQQQmhEkgchhBBCCCGERiR5EEIIIYQQQmhEkgchhBBCCCGERiR5EEIIIYQQQmhEkgchhBBCCCGERiR5EEIIIYQQQmhEkgchhBBCCCGERiR5EEIIIYQQQmhEkgchhBBCCCGERiR5EEIIIYQQQmhEkgchhBBCCCGERiR5EEIIIYQQQmhEkgchhBBCCCGERiR5EEIIIYQQQmhEkgchhBBCCCGERiR5EEIIIYQQQmhEkgchhBBCCCGERiR5EEIIIYQQQmhEkgchhBBCCCGERiR5EEIIIYQQQmhEkgchhBBCCCGERiR5EEIIIYQQQmhET9sBaFOXqk6YW1hoO4xyZ/6WG9oOodyKCrmr7RDKrbUWhtoOoVxr7Gyt7RDKLUsTfW2HUG7pKLQdgRBCFCYtD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IietgN4kSz+4Wu+mD0DgB0HTtC4aXO1MslJSXyzcC57dvxF9MP72Ds40atvf9776DPMLSzKOuT/xNxIjyndq1HP1ZLKNiZYmugTl5pFSHQq60+Fsf/ag0LlhzSvTOfajlRzMsPWzIDcXCWR8ekc8n/I6hOhJKZnFypfq5I53es60aa6Ha62xpgb6XM/MQOfgGgWHwrmQVJmWR7uP2JpYsDMoY1oXNUedwczrM0MiU3K4PbdRH7dd5O/zoSqbePlbMGHL9enVS1HXGxMiU/J5GZkAkv23GD3hXC18lamBnw0sAF9mrlT2c6UpLQsTt64z9zNl7gZkVD6B/kf2Joa0L6qDc3crXGzNsbaRJ/kjBz87yez+dJdbj1IKVT+0OSWz9znsNUXiU7JKvjbQFeHPnUd6VLdDicLIxQKeJCcyfHAWLZfu09qVm6JH1dp+mvLej6d9uZTyzRv3Z4Vm3cV/B12J4hlP3/NpXNneHAvCksra6pUr8mIV9+gY7eXSjvkMrV543pOnzqJ3+VL3Lh+jaysLBb/toIRo8Y+c9vQ0BBaNalPamoqr46bwPc/LSn9gMuBhIQE5n4+k4sXLhAaGkJCfDy2dnZUr16DNyZOot+Al1EoFNoOU6sunD/PvDmzOHvGl6ysLGp512byO1MYOmy4tkPTOqmbom3csJ5TJ324fOki1/N/i35bvopRY8ZqOzSNSfJQRgIDbvL1gjmYmJqSlppaZJm01FQG9u6C/7UrtOvYmf4DB3Pj+lWWLf6R0z7H+WvvUUxMTcs48n/PxtSAV5pVxi8sgYP+D0hMy8bWzIBO3g78MqYRm86E8+kf/gXl+zV2wdJYnwt34nmYnImBrg4N3a14u2tVXm7iwsAfTxOT/Pjmb+7AOtRzteRqZCK7Lt8jKzeP+m5WjGjlTs96zgz95Qx3oouua22ztTBkdKfqnAt8yM5zYcSnZGJvaUyvJm5s/KAzKw7eYvLSUwXlm1azZ9/nvdDX1WH3hTD+OhOKvYUx/Vq488fHXZm76RLzf79cUN7GzJBjC/pQrZIlZ249YNf5MJysTejfwoNuDSvTc/ZezgdGa+PQNTKgnhNDG7sQlZDBxYhEEtKzcbE0onUVG1p52jD/QCDHg2ILyq89F1HkfipZGtGlhj1hcWmFEgddHQXfDPCmlpM5QdGpHLgVDSip72LJay3d6Fjdjsm/XyMzJ6+0D7XE1KxdjzenflzkuoN7/iIo4Cat23cuWHb10nleG/wSOTnZdOjai669+hIbG83hvTt5+7WhTJr2CZOmFb2/imje7JmEh4dha2eHk5Mz4eFhGm2nVCp5a8LrpRxd+RQbE8Pa1ato1rwFffr2w9rahujoh+zdvYvhQwfx6uvj+GXJb9oOU2tOHD9Gn17dMTAwYNDgoVhYWrL9r628OnoE4WGhfDj9E22HqDVSN8WbPetTwsPCsLOzw8nZmfAwzX6LyhOFUqlUajsITaxfvx4fHx8uXrzItWuqTG3VqlWMHTv2H+8rKSkJS0tLboVFl8nT/NzcXPp2a4dCocDTqxpbt/yvyJaHrxfM4buvvmDSO+8x4/P5asunfjiD9z+eWerxtpt3uET2o6MAhUJBbl7hU8zUUJc/3m5JNSdzei7yITD/KbKBng5ZRdysTelejcldq7Ls2B0W7gooWD6qtRvHb8UQHptWqPyEjlX48KUaHL35kPErLpbIsTwSFXK3RPajo6NAAWp1Y2akz/Ev++Dtak2jKX8WtBBsm9GNHo1ceWXBwUKtDK52ppz/9mX09XRwHr2uoP6+G9eSiT29+WHHNaavOVdQvnl1Bw7Ne4nbdxNpMnUrJfntb93Ru8T21aaKDQnp2Vy/l1xoeR1ncxb19yY9O5chKy+Snff0A5jczoP+9ZxZcjKUP/3uFSxvX9WWz3pUxyc4ls/33i60zeyeNWjjZcPCg4EcDIgpsWP6YUDdEtvXP5GdlUXHxtVISU7i0PkA7OwdAHhz1EB8jh7gp5WbCrUy3IuKYECXFuRkZ3PaPwIDQ8NSj9HV1qTUP+PokUN4eVXDzd2dbxct5POZn2jU8rD0l5+YMf195nyxkE8+eq/MWx70dbX3ZD83NxelUomeXuHnjMnJyXRo05KbN29w4fI1vGvX1kp82mz1yMnJoX6dmkRFRnLMx5cGDRsC+XXTtiW3AwK4fPUGVatV01qM2iJ183RHDh/Cq2o13N3dWfTVl8yc8XG5aHlISkrC0daSxMRELJ5xb1xhxjx8+umn/Pbbb4SFheHs7KztcP6RX77/mhvXr/LNT7+hq6tbZBmlUsnGdaswNTNj6oczCq2bPPVDrKys2bh+NRUk1wMgT6l+cwyQmpmLT/5Nmbvd45uGohIHgL1X76vK/u0GY92pcLXEAWD5sTukZeXQrIrNv469tOXlKYusm5SMbA75RQHg5fT4y+vpaE5enpIDfpGFykfEpHIjIh4TQz3MjfULlvdp5k5ubh5zN18qVP7s7YfsuRCOt6s1bWuX3+/RyTtxaokDwPV7yVyJSsLCSB9Pu6ffcOrrKuhU3Z6s3DwO3SrcyuJsobohPheWoLbdubB4AKxM9NXWVUSH9u0kIT6Odp17FCQOAJHhISgUCtp06FqovLOLK1Vr1CIjI53UVPX/BxVVx05dcHN3/0fbBAcH8fnMT3h32gfUa9CgdAIrx3R1ddUSBwBzc3O6dOsGqOroRXTs6BHuBAczZOjwgptjUNXN9E8+Iycnh7VrVmkxQu2Runm6Tp274P4Pf4vKmwqTPCxfvpzQ0FCio6OZOHGitsPR2K0b/nz31Tzeef9jatQq/snsneBA7t+7S9PmLdW6JhkZGdG8VRvu340i5E7F/6E20NOhRVVb8vKUBa0OT9Oxlj0At+8/uyyAEsjLKzpxKe8M9XVpX8eZvDwlNyMTCpbfjIhHR0dBl/ouhcpXtjXF29Waa2FxxCY/HuPhYGlMTHImqRk5ap8R+lBVjx3qlN/k4Wly8v+/Puv/b1svWyyM9PANiSfxb/UQGpcOQFM3K7XtmrpbkadUciUqqWQC1rKtm9YCMHD4mELLvarXQqlUcupE4ZbGe3cjCQq4SbWatbG2sSuzOMubvLw83prwOq5u7nz0yWfaDqdcycjI4NjRoygUCmo95br2PDtx/BgAnbt2U1vXJX+Zz4njZRlSuSF18/yrMGMeunTpou0Q/rGcnBymvjWOqtVrMnnKB08tG5L/9MazStUi1z9aHhIcRBWvitXUZ26kx6vtPFAoFNiaGdChpj2VrI358UAgYTHqLQcvN3Ghso0xpoZ61HaxoEVVW/wjE1l5IkSjz+tZzwkzIz32XLn37MJaZmliwOTetdHRUWBvYUSPRq642psxb/Mlgu89vnn9fOMlWtZ0YtMHndl1Ppzg+0nYWRjRr7kH4TEpjPzmSKH9xiSl42BpjKmRnloC4eFgBkC1Spalf4AlzMHMgEaVLYlNzSKkiFanJ/WopXrKvvfGA7V1Z0LjOR0SR7uqtiweXJerd1V1Xb+SBZWsjPjxWAi3H5bP8TL/xN3IcM6ePIajUyW1Foa3P/iMS+d9mTJ+BJ26vYSrRxXiY2M4tHcnzi6ufLN0jZaiLh8W//QDZ8+cZv/hExiWQdet8iwhIYGff/yevLw8oqMfsn/fXiIjIvjk05kvbNeToKBAAKpWVT9+a2tr7OzsCM4v86KRunn+VZjkoSL66duF3Lh+lV2HTqKv//QuEMlJqpsXc4uib+jM8vufPSpXkVgY6/NOt8c/Ilk5eSzYeYsVx4tOBgY2daG5l23B3z4B0by38SpJ6epP0f/O2dKIz/p5k56Vy3f7yv+Pk6WpAZ8OaVTwd1Z2Lh+vOcv3O64XKncrMoH2H+9gw3udeLmVZ8HyuORM1h25TdC9wufF/suRjO1cgxmDG/LJ2vMFy5tUtaNnY7eCz65IdHUUfNS1GgZ6Oiw7GsbTGh6czA1pUNmCB0mZXAxPLLLM7D0BjGvpzisNnamen1ABHLwVzfnwhBKOXju2bV5PXl4e/YeMVOsy6VW9Jv/bcYRpE0dzYPdfBcstLK0ZMGQk7p5FP8h4EQQF3mbe55/x5lvv0KzFs2fyet4lJiQwf96cgr/19fWZ/+VXvDv1PS1GpV1JiarfFUvLoq/Z5hYWREVGFrnueSd18/yT5KGU+F+7yg9fL2Di5KnUrd/w2Rs8x6Li06n6/l50FOBsZUzvBs6817M6jTyseGedn1r3kxFLVAN8rU30qe9uxUcv1WDHlNa8vuICAUX0g3/E0lif5eOaYGtmwAebrhJSTmdaelJ4dArGA1ego6Ogsq0pg9tUYfbwJrSo4ciIb44U1E0jLzt+/6gLNyISaPn+XwREJeBgZcwbPWrx1astaF3LiaGLHnc/mbfpEt0bVmZqv3o0r+7AudvROFkbM6ClJzcj46nnYVuhunUpgPc7eVHfxYLd/g849IyBzD28HdBRKNh/6yFFHaWBrg6fdq9GTUcz5h8I5FJEIkolNKxswVvtPGnqbsXbv1/jXjme7vdZ8vLy+GvLehQKBQOGjFJbf/3KJd55bSheNWqxZa8PnlWrExv9kE1rlvHV5x9z8expflj+Py1Erl15eXm8Of41nJwr8ensudoOp1xw9/AgLSuP3NxcIiMi+H3LJmbP/JQzvr6s37i5yHERQojnV4UZ8/BfZGZmkpSUVOhfaZsy6XXcPaswbbpmfWXNC1oWin5KmlLQMlGx3vXwpDylKpH49egdvt13m+51nRjS3LXY8vFp2Ry7Gc1ryy5gbarPF4PqFFvWwliPtW80pZqjGTO3+rP9UsnMilRW8vKUhEen8PW2q3y+8SL9WnjwWtcaAOjpKlg3rSNKYPDCg/iFxJKelUvYwxQ+WXue30/eoV8LD9o9MYYhKi6NNh/tYNWhADwczJnUy5tm1R2Yu+kSi/68AkBMYoY2DvVfmdbJi6417Tl4K5rvj955alkF0K2mPbl5SvbdeFhkmWGNXWhVxYbvjt7hWGAsSRk5JGfmcCI4jq8PB2NlrM+oZpVL4UjKju+JI9yLiqB56/ZUdvMotC47O5v3J41FoVDw44qNeNdtgLGxCZXdPHj/sy/o0Xcgh/ft5NypE9oJXouW/vIT58+d4aclv2FiUvqzQFUkurq6uHt48P6H05n1+Vx2bN/GyhXLtB2WVljkP1VPTCz6mp2cP6vji0jq5vn3QiQPCxYswNLSsuCfq2vxN6wl5cb1qwTdDqCKkwUu1oYF/37fuA6Avt3a4WJtyL7d2wHw9Mof01DMgOhHyx+Vq+hO5j85bu717BmR7iVmEPwwlXqVLTHSVz9lLY31WfdGM2pXtuTzv26w6UzRc/5XFI9mW2qXPxtSDRcrqjhZcD7wIelFvLjs2HVVotSoim2h5Xfj0pi05CReEzZhOXQ1td/6nW/+ukqNylYAXAouuWlIS8ujFoee3g4cuR3DosNBRbYkPKmpuxUO5oZcikjk4RPvdnhScw8rAPyi1C9ufpGJ5CmVVLM3U1tXkTwaKP3ysDFq60KCbhMZFkLdhk0wNla/QW7eqh0A/tcuq6173l276odSqaR3985YGusW/OvdXfWOjFXLf8PSWJfhgwZoOVLt6twlf+Dr8Rdz4Ouj/vxBRfTdj4+PJyYmBq8i+vy/CKRunn8vRFvjxx9/zLRp0wr+TkpKKvUEYtioV4tcfua0DyHBQXTr2RtbO/uCJ4JVvKrh5FyJ82d9SUtNLTTjUkZGBmdPn8TJuVKxA6orGgdL1QDEHA27ztibGxbMovQkS2N91r7RVJU4bPNnw2n1Ny1XNM42qpu5nFzVwRroqRImOwvjIsvbWRgBkJn97Bea6egoGNSmCtk5eUW+xbo8UQDvdfKih7cDR2/H8OXBwKeOc3ikp7dqoPSeIgZKP6Kvq6pTK2N90rMLd02yMNZHR6EgO7fivCDu7xLiYzlyYDeWVtZ06dFHbX12tiqpio8rOoGMi1O9gM/A4MUbKNy6bTt0i+iG8+D+PQ7s20v1GjVp3rIV9V/w7qj37qkeWryoXZbatmvPooULOHzwAIOHDC207tDBAwVlXkRSN8+/F+Jbb2hoWOazZXz949Iil0+ZNI6Q4CAmT/2w0EviFAoFw0a9yndffcF3X31R6CVxP3/3FQkJ8UydMEmrL8X5p2pVMiciLp2Uv832Y2msz/s9VV1yTuTPv29loo+9uWGRU7e+060q9haGnLodQ9YTN3SWxvqsm9gMbxcL5vx1g3WnKk7iUM/DhtCHySSlZRdabm1mwOfDmwCqQc8A/uHxJKRm0rKGA53ru3D4SlRBeWdrE97oXguAE/6PZ5fS01Wgp6tDxhMtFQoFfDm6GTVcrPhx53XuxT99tiJtUgDvdfaiRy0HjgfGsEDDxMHSSI8WHtYkpGfjGxJfbLnr95LwtDVhVLPKfH04uGDfCmBsflc6vwo8VevOPzeRnZVF75GvF/mSt2o1vDG3sOTy+TOcOn640JunH96/x+Y1qq4oTVu2LbOYy4uRo19l5Gj1hz8+J45xYN9eWrdtV6YvidOmK35+eHh6qnUxiYuLY9ZnqvcRdeveQxuhaV3HTp3xrFKFzZv+x6TJ71A//z0gycnJfDl/Lnp6eowaPVarMWqL1M3z74VIHiqKSe+8x4G9u1j84zdcv+ZHvfqNuHH9KkcO7ad23fpMeqdizWzxcpPKDG5embPBcUTFp5OWlYuLlREdajlgZqTHvqv32XFZ9fTK2cqIndPa4BeeQNCDFGKSMrE2NaBJFWu8HMx4mJTB7G03Cu1/8diGeLtYEPQgBSsTfd7ppt4qs+pEKMlFvOtA20Z1rMbYzjU47n+PiOgUUjNycLM3o0fjypgbG7DNN4TNPsGAanaq6WvOsXRSW7bP6MbeSxEERCbiYGVE32YeWJoasHTvDfzDH98sO1oac/H7gRy+EkXow2QM9HTo0qAyNStbsedCOJ+tP19caOXCqGaV6VHLgbSsXCITMhjZVH38wak7cQT/barfrjXt0dfV4eCt+09t1frfhShaetrQraYD1ezN8ItMRAk0cLGgip0p95Iy2Hwxqtjty7utG/Pf7VBElyUAA0ND3v/sC2Z9MJk3R71Mu87dqVK1BrExDzm8bxcpyUkMGzOB6rW08+bg0rBm1XLOnD4FgL+/ajaztatWcjJ/vvmX+vSjd9/+2gqvXFq/bjWrV66gXfuOuLm7YWpiSnh4OPv27iYlJYX+AwYyZNhwbYepFXp6eiz5dTl9enWnS8e2DB4yDHMLC7b/tZXQkBBmz5lHterVtR2mVkjdPN2qFcs5feokAP7Xr6mWrVxe8H6MPv3607dffy1FpxlJHsoRE1NT/th5kG+/msfu7dvwPXkCewcnxk96h2kffqr28rjybt/V+5gb69HAzYqmntYYGeiSmJbNxdB4tl2IYpff4yflUfHpLDkcTHMvGzrUtMfSRJ/M7DzCYlL5+WAQq31CSfjbU3oXa1U3nqqOZoWmgn3Sn+ejymXysM03FAsTA5pVd6BNLSdMDPWIS8nk9M0H/O94EFtOFh4UvObwbcIeJjO5dx2aVXegRyNXUjNyuBYWx6pDAfzveOGxMolp2ew6H0bLmo70bOxKdm4eN8LjeXOxD2uO3Ka8v6jc0Vz1tNzEQJcRRSQOAPeTMtWSh0ddlvYWM1D6keiULN7cfJXhjV1o6m7FS3UcQQkPkjP5/fJdNl6MIqkcnjeauHb5AoEBN6jboMlTb/4HDhuDi6s765b/wtVL5/E5cgBjE1NqeNfh5WFj6PfK83VTeOb0Kf63fm3hZb6nOOOrSijc3D0kefibAS+/QmJiEufPnuHUyROkpaVhY2NDy9ZtGDFiFIOGDK1QreElrX2Hjhw+dpJ5c2bx5x9byMrKopZ3bWbOnsuw4SO0HZ5WSd0U7/Spk6xfV/g9Or6nT+Gb/3DD3cOj3CcPCqWyvN9GqCxfvpyTJ1WZ2rVr17h06RKtW7emalXV0+b+/fvTv39/jfaVlD/S/1ZYdIWevai0tJt3+NmFXlBRIRVrFqey1Lrji/mmWU39MKCutkMot1xtZVaj4ujrvrg358/yIicuQpS0pKQkHG0tSUxMxOIZ98YVpuXh5MmTrFlTOFM7deoUp06pMjUPDw+NkwchhBBCCCHEP1dhkofVq1ezevVqbYchhBBCCCHEC+uFeM+DEEIIIYQQ4r+T5EEIIYQQQgihEUkehBBCCCGEEBqR5EEIIYQQQgihEUkehBBCCCGEEBqR5EEIIYQQQgihEUkehBBCCCGEEBqR5EEIIYQQQgihEUkehBBCCCGEEBqR5EEIIYQQQgihEUkehBBCCCGEEBqR5EEIIYQQQgihEUkehBBCCCGEEBqR5EEIIYQQQgihEUkehBBCCCGEEBqR5EEIIYQQQgihEUkehBBCCCGEEBqR5EEIIYQQQgihEUkehBBCCCGEEBqR5EEIIYQQQgihEUkehBBCCCGEEBqR5EEIIYQQQgihEUkehBBCCCGEEBqR5EEIIYQQQgihEUkehBBCCCGEEBqR5EEIIYQQQgihEUkehBBCCCGEEBrR03YA2mRkoIuxga62wyh3tr7bRtshlFu/nAnXdgjl1p+7r2k7hHLtOwdzbYdQbk1r46ntEMqtSlZG2g6h3DLQk+efxdHRUWg7BPEck2+eEEIIIYQQQiOSPAghhBBCCCE0IsmDEEIIIYQQQiOSPAghhBBCCCE0IsmDEEIIIYQQQiOSPAghhBBCCCE0IsmDEEIIIYQQQiOSPAghhBBCCCE0IsmDEEIIIYQQQiOSPAghhBBCCCE0IsmDEEIIIYQQQiOSPAghhBBCCCE0IsmDEEIIIYQQQiOSPAghhBBCCCE0IsmDEEIIIYQQQiOSPAghhBBCCCE0IsmDEEIIIYQQQiOSPAghhBBCCCE0IsmDEEIIIYQQQiOSPAghhBBCCCE0IsmDEEIIIYQQQiOSPAghhBBCCCE0IsmDEEIIIYQQQiOSPAghhBBCCCE0IsmDEEIIIYQQQiOSPAghhBBCCCE0oqftAJ5Xd+9GsX3rHxzcv5fA2wE8fHAfa2sbmrVsxTtT36dJ0+Zq2yQlJbHwi8/ZuX0bDx/cx8HRiT79BvDRjFlYWFho4ShKj1Kp5PC+nWxc/SuhQbdJSU7CsZILTVq05bU3p1DZ3bOg7JJv57P0+y+L3I+BoSHnA6PLKuwSY2WkR2NXC+o4meNkYYCFkR6pWbkEx6SzPyCG0Lj0QuXbeFpTv5I5lSwNMTfUI0+pJCY1myt3kzh8O4607NwiP6epqyWdq9tQycKInDwld2LT2On/kLD4jLI4zH/NwkSf6QPq0tDTBjd7U6xMDIhLySToXjIrjgSy60Jkkdu52ZkytY83HWo74WBpRGJaNrfvJrLySBA7zkeoldfX1WFcl2oMbOFOVSdzACJiUzkdEM1H6y6W6jH+W1bGejSpbEldZ3OczA2wzD93gmLT2HcrhpC/nTttq1jToJI5lSyMsDDSIzdPSWxaFn5RyRwKjCU1q/C5U9nSiK7VbXG3NsbKWA9DPR3i03MIi09n363ocn/uFGXb5vV8Om3iU8s0b92elVt2F1oWGR7Kbz99zenjh4mJfoCFhSVVqtVk2JjxdO/zcmmGXKY2b9yA7+mTXLl8iRv+18jKyuKXpSsYPmpMkeWDgwL5dtGXnPE9xd2oSKytbahRsxbj35xMr5f6lHH05cO3X3/FZzOmA3D0xGmaNW+h5Yi078L588ybM4uzZ3zJysqilndtJr8zhaHDhms7NK2r6HUjyUMpWbbkZ374dhGeVbzo0KkL9vb2BAcHsWfndvbs3M6y1RsYMHBQQfnU1FT6dO/Etat+dOjUhYGDhnD92lWW/PwDJ08cZ8+h45iammrxiErWN/NmsG7Zz9g7ONGxe29Mzcy5ffMaWzeuZt+OP1iz7SDVangX2qbvK8OpVNmt0DJdvYp5CnesZkOPmvY8TMnkxoNUkjNycDA3oEElCxq4mLP8TCQXI5MKyrdwt8TEQJfA6DSSMnLQ01HgaWtMb28HWrpb8eXhEJIycwp9Rs+advSv60hsahYnguMw1NOhqZslH3T05EefMG5Hp5X1YWvM1syQ4W09uRgcy95LUcSnZGFvYUi3Bi6sntyGtceCmbb6fKFt2td2ZO07bQE44BdF6MNUrEz18Xa1or23o1ryYGmiz5b3OtDYy5azgdGsORYMgJu9Kf2buZXb5KFzVVt61rLnYXL+uZOZg4OZAQ1dLGhYyYLfzkRw4Ylzp6W7FSb6ugTGpJKYf+5UsTWhT20HWnlY8cXhOyRlPD53PG2MqetsTnBsGgHRqWTl5GFnZkB9Z3MaV7Zg5dlIzoQnauPQ/7WatesxadrHRa47sPsvggJu0rpDl0LLT584wjuvDQWgQ9eeVHbzJCkxnoCb/vj6HH2ukocv5swkIjwMWzs7HJ2ciQgPK7bshfNn6duzC9nZ2fR8qQ99+g0gJjqanTu2MWLwAKbPmMlHn8wsw+i179bNm8ybMwtTU1NSU1O1HU65cOL4Mfr06o6BgQGDBg/FwtKS7X9t5dXRIwgPC+XD6Z9oO0SteR7qRqFUKpXaDuJZoqKi+P3339mzZw+3bt3i/v372NjY0Lp1az788EOaN1d/iv80SUlJWFpaEnovrtSe6O/cvg07Oztatm5baLnvKR/6v9QNM3NzbgRFYGhoCMCCebNZtGAe70x9n9nzHj9lf7T8g48/5eNPZ5dKrH8XEVu6N5UxDx/QtVkNnFxc+X3fKczMH/8/WL/8FxbN+Zh+g0cy5+vFwOOWh+Wbd9O0ZdvidlsmfjkTXiL7aehiTnJmLkExheu6qp0JU9u7k5Gdx0e7bpOTp/p66ukoCv77SX1rO/CStz0HAmL48+qDguUOZgbM7l6V6NQsFhy6Q0ZOHgDOFoZ83LkKiRnZzNoXRBG7/Nf+3H2txPalo1CgUEDu3wI0M9Jj32ddqeliSetP9hBwV3WTXMnGhJPzehCdlMHLXx0jKq5wverqKNT2tXpya3o1qsybv53hzzNhzyz/X/XvVadE9tPIxYLkzBwC/3buVLMz4b32HmTk5PH+zoBnnjv96jjQx9uBfbei+eOJc6e48pUsDPm0ixcZOXlM23GrRI7lkWltPJ9dqBRkZWXRsVFVUpKTOHwhADt7RwDuRUXSv3MzbO0cWL55J5VcXAttl5OTg14ZPbioZGVU6p9x7MghqlSthpubO999vZA5s2YU2/IwaEBvDh3Yx4Yt2wq1MkREhNO6WQNysrMJiYopuLaVJgM97fe8zs3NpWO7VigUCqpWq86m/60vFy0POjoKrX12Tk4O9evUJCoykmM+vjRo2BCA5ORkOrRtye2AAC5fvUHVatW0FqO2lOe6SUpKwtHWksTExGfeG2v/m6eBn376ialTp3Lnzh26du3Ke++9R5s2bdi+fTutWrViy5Yt2g5RTZ9+A9QSB4CWrdvSpl0H4uPiuOGvutlSKpWsX70SMzMzPvj4s0Llp74/HStra9avWUUFyPM0cjcyjLy8PBo0aV4ocQBo27k7AHExFa8r0j9xOSpZLXEACIpJI+BhGmaGerhYPr74FnUzB3AxUvUE2N7MoNDyVh5W6Ooo2HszuiBxALiXlMmZsAQczAyp4VB+W7LylMoib95TMnI4eu0eAFUczQuWT+1dCwsTA95fc0EtcQD1JKRRFRt6N3Hld99QtcShqPLlyaWoJLXEASAwJo1b0amanzsRqnPHwazwTV5x5e8mZXIvORMLIz2M9SvEpeOZDu/dQUJ8HO279ChIHACW/bSIlOQkZn75vVriAJRZ4lBWOnTqgpubu0Zlw0JDUCgUdOnavdByV1c3annXJj09nZTk5NIIs1z69uuFXLt6hSW/rUBXV1fb4ZQLx44e4U5wMEOGDi+4OQYwNzdn+iefkZOTw9o1q7QYofY8L3VTIX4BmzVrxokTJ2jbtvDNuI+PD507d+bNN9+kX79+ZfKkoyTo6+sDoKerqv7goEDu3btLpy7d1LomGRkZ0ap1W/bs2sGd4CC8qlb8TN3N0wt9AwP8LpwlNSUZU7PHN4EnjxwAoFnr9mrbXTp3mut+F9HR1cXTqxot2nTEoIL8P/8nHt245uY9oyBQ11lVd3cTMwstr26vOo9u3FdvQve/n0J7Lxuq25ty80HFamI31NehjbcjeXlKAu4+7jrTr5kbscmZ+Nx8SH13a1rVdEBHAdfCE/C5+YC/590Dmqm6v+04H4GNmQE9Grpgb2HE3bg0Dl29R3xqVlkeVol5dO5okvs8OneikjQbw2BvaoCTuSGxqVmkZ2twclYAWzetBWDgsLEFy5RKJft2bsPK2oYWbTrgf/Uy531PolTmUdO7Hs3btEdH5/lInv6NmrW8CbwdwJHDB+jRs3fB8sjICG7e8Me7dl1s7ey0GGHZ8fe/zvx5c/jo4xl4e9fWdjjlxonjxwDo3LWb2rou+ct8Thwvy5DKjeelbipE8vDyy0X3LW3bti0dO3bkwIEDXLt2jSZNmpRxZP9cZEQ4x48extHRCe86dQG4ExwIUGxiUMWrqqpcUOBzkTxYWdvy9gcz+faLT+nfqSkduvbExNScoAB/zpw8xsDhrzJs7Btq2y3+5otCf9s7ODH326W0bNeprEIvddbG+tRyNCUxPZuoRPWbupbuVtia6mOkp4ObtTE1HEwJj0/n0O2YQuUczA3IyM5VGwcB8DBFdWPs8LfWivLIwkSfid1qoKMAOwsjutRzprKtKV/9dZ07D1IAcLc3xcbMkEt3Ylk0ujGvdir8HbkaGseIH3y4F/94IHEDTxsAPB3MWDyhBZYmj+siJT2bKavO8dc59QHW5ZmNiT7ejmYkpGcTWcS508rDCjtTg/xzx4iaDmaExadzMCCmiL2Bq5URDV0s0FWArakB9Supko31F++W6nGUlbuR4Zw5eQxHp0q06di1YHlkeCiJCXHUadCYOdPfZfO6FYW2q1WnPj+v2oJTJZeyDrlc+OSzzzlz+hSjhw2iV+++eFbxIjYmhp07tuHq6s6qdRu1HWKZyMnJ4Y1xr1KjZi3e+2C6tsMpV4KCVPc0VYu4X7G2tsbOzo7g/DIvmuelbipE8vA0BU/xK0AzcnZ2NhPHjSEzM5PZ874saOJMSlT12y6uj5l5/vKkpKQi11dEY954B3tHJ+Z+PIUtT1ycGzRuTu8BQwr+vwLUqF2Ped8upXGLNtjaOfDgfhT7dvzJip+/4d3Xh7Ju+2FqeNfVxmGUKB0FvNbcBX1dHf68epeiHh639LAq1N3I/34Kq85Fkva3J8HG+jokZxQ9A1NG/sxMxvrlv4nd0sSAD/s/HiuQlZPLrE2X+WVfQMEyOwtVn/B67tZUd7Zg8vKz7L0UiYWJAVN7ezO6gxerJremx9xDatvMHtKAP3zDWLT9OvGpWXSrX4mvRjVm8YQW3L6bxI3IijEwWFcBrzerjL6uDn9cvavW0gLQ2sO60Llz/X4yK86qnzuPuFoZ0be2Q8HfienZrDgXxY38pK2i27Z5HXl5efQfMrJQd5NHXSZvXvPjTmAA875dSqfuL5GSnMRvPy3ijw2rmTphJBt3HdVW6FpVs5Y3B4+dYuzIoWzf9mfBcitra0aMGvNcPODSxKKF87l29QrHTp4pdL0SkJSo+t20tLQscr25hQVRkUXPmPe8e17qpvzfcT9FeHg4hw4dwsnJibp1y/fNY15eHm9PHMfpkz6MfnUcQ4aP1HZIWvXbj1/x6w8LeXPqx/QZOAxzSysC/K/x9dyPGTf0Jb5avIYuPfsC0Kl770Lbunl4MeGdD7G1c2DO9HdY9uMivl66VhuHUWIUwJimLlS3N8XnThxni5nN5tvjoQCYGujiaWPMwHqOzOjixU8nw4j6W9el50FETCp2Yzeho1DgYmvCgOZufDKwHk2r2vH64tPk5il5NC5QT1eHBdv82HQyBIDEtGymrT6Pt6slTbzsaF7NjrOBqqfsj7a5EZHA5OVnCz7vD98wzIz0+HpMUyZ0rc6UVYVndCqPFMDYppWp4WDK8eA4zoQlFFlu0TFVvZgZ6OJpa8wr9Zz4rGtVfvQJJbKIc+d0aAKnQxPQ01HgaG5At+p2TGnrzh/X7nMgILYUj6j05eXlsW3zehQKBS8PHa22DlQDYd/+4DMGDFH9VltaWfP5Vz9z+4Y/Vy+f5+K50zRu1qrMY9e2y5cuMGLwy9Ss5c2xk+eoVqMm0Q8fsPy3JcyY/j6+p0+ybuMf2g6zVF29eoWFC77g3anv0bBhI22HI0SZq7AdN7Ozsxk1ahSZmZl89dVXTx2olJmZSVJSUqF/ZUmpVPLuW2+wZdMGBg8dwbc/Li603sLy6S0LyUlPb5moaM6eOs4vX89j6JgJjJv8Po7OLpiYmNKwaQt+WvU7hkbGfD2n6GkVn9T3leHo6enhd+FMGURdukY2qUQLdyvOhCWw4eK9Z5ZPzcrl+v0UfvQJw8xQl1GNKxVan56dV+ygVqP8Fof0Yt4NUR7lKZVExKTy4+6bLNh6jd5NXBnd3guApLTsgnL7LkepbbvfT9XN5lFXpSe3OXBFvQvOvsuqZfWfKF+ejW7iQksPK3xDEzTqUpSSlcu1eyl8fyIMc0NdRjd5evebnDwlUYmZrDofxfX7KbxS14lKFhV7rNHpE0e4FxVB89btqezmUWid+RO/sx279VLbtkPXngD4X7lcqjGWR9nZ2bw2ejgKhYL1m7dSv2EjTExMcPfwZO78rxjwymB27fgLn+PPd6vMhNfHUqWKFzM+m63tUMoli/yn6omJRT8ES86f8fJF9LzUTYVMHvLy8njttdc4ceIE48ePZ9SoUU8tv2DBAiwtLQv+ubqqz55RWvLy8nj7zfFsWLuKgYOG8stvK9UG21XxUjXzFtfP7U5wkKrcc9Ic7HNkPwBNW7ZTW2dja0e1mt7ci4ogPu7pTzf1DQwwMTUjIyP9qeXKMwUwukkl2nhacy48gdXnoorsrlSc+PQc7iVl4m5jjL7u46n5HiZnYaSvi4WheuPio7EOj8Y+VDRHr98HoHVNVZeakIcp5OSPLk98IpF4JDFNdZxGT3TTCrqfXGz5pPzy5b1bl6rFwYW2Vaw5G5bAyvOR//DcyeZeUiYeNsYY6Go2raP/gxR0dBQFA/Irqq0b1wAwcLj6VKRuHl4FD6PMLdQv4ub5F/bMCvy782/dDrhFaMgdGjdthomJidr6du07AuB3+VJZh1amrl29QkDALWwsjDE11Cn4t2Gd6rzq2K4VpoY67Nz+l3YD1ZJH/fmDiriniY+PJyYm5oXp3vZ3z0vdVLjkQalUMn78eNavX8/IkSNZunTpM7f5+OOPSUxMLPgXEVE2AyHz8vJ4Z9IE/rduNQNeGczSFWuKbCHxqloNZ+dKnDtzWu0FMxkZGZw+5YOzc6WCgdMVXXa26uYsPq7ogZrxsarlBgZPH9AbFhJEUmKC2ovjKgoFMKpJJVp7WnM+PJGVZ/9Z4vCIpbEqQXiyn/vtaNV55O2kfpNX28msUJmKxsnKGICc/O4lWTl5nA9SnTM1Kqm3ztWopLrZi4h5fLw+Nx8UX95FVT48pvzWz6Nubo+SzuXnIosc5/AslkZ6oNRsdiZQvd0aILcCTxudEBfLkQO7sbSyoUuPvmrrDQwNadBE9e6g4ED191kE31Ytq+RaMX93/otHv90xxUyl/Wh5RZn58N8a8+prRf57dGP4Uu++jHn1Ndw8PLQbqJa0baeaLfHwwQNq6w7lL3tU5kXzvNRNhUoe8vLyeP3111m5ciXDhg1j9erVGk2ZZ2hoiIWFRaF/ZRHr22+O53/rVtPv5Vf4dcXaYrtWKRQKRo59jZSUFBYtmFto3Xdff0lCfDwjx76GQqG9l76UpAZNVC/PWbfsZ5KTCjfd7fh9A+Ghd/Cu2xBTM3NSU5K5ffO62j6SEuKZ/cFkAHr0faX0gy5hj1ocWntacyEikZXnin9qbGqgi3Mx3UR6e9tjaaRPwMPUQvPznw5NIDdPSc9a9hg98SIlZwtDWrhb8TAlk4CH5ffmuI6bFebG6oMQrUwNmPFKPQAOX33cvWvVEVXr3If96xR6cVRVZ3OGtvEkOT2bw9cel99xPoKYpAwGtnSnVuXHT5f1dXUKBmhvP18+Z1t61OLQxtOa8xGJLD9bfOJgaqBbbBejvrUdsDTW51Z04XOnqq0JRb1fytXKiPZVbMjJU3LjfsUdNL3jz01kZ2XRZ+CQYqd6HjJ6HACLv5lPVubj8SB3ggLYvmUDpmbmtOnQtchtn2e1vOtgYWnJWd/THDlU+Obn3r27rPxN9TCvddvyf/PzXyxeurzIf81bqsbAvP/hdBYvXU79+g20G6iWdOzUGc8qVdi86X9c8fMrWJ6cnMyX8+eip6fHqNFjtRafNj0vdVNhBkzn5eUxbtw4Vq1axZAhQ1i3bl25fiHLVwvmsnH9GszMzKhatRpfL/xCrcxLvftRN//H5Z2pH7Bv9y5+/O5rrl7xo0HDRly/dpVDB/ZRt14D3pn6QRkfQenp9tIA/tiwigu+PvRp35AOXXthYWlFwI1rnPE5ioGhIR/MUr1lOyE+jkHdW1G7XkOq1qyNja09D+/f5dSxgyTEx9GibUdGjXtLy0f0z73kbU8rT2sysnN5mJxFr1r2amX8opKJTMzA2lifz7p5ERKbxr2kTBIzcjAz1KOanQlOFoYkpmez8VLhcRIPU7LY6f+Q/nUdmdnNi0uRSRjo6dDMzRJdHVh34W6Jvl26pA1t48nIdlU4deshETGppGXm4mpnQtd6lTAz1mfH+Qj+eOLlblvPhvNSk8r0a+rG8bk9OHLtHhYmBvRpXBkjfR3eWna2UBellIwcpq4+z6q3WrPvs67sPB9BQloW7b0dqVXZioNX7rLRJ0Qbh/5MfWo70Dr/3HmQnElvbwe1MpejkohIyMDGRJ9Z3apyp9C5o0s1O1OcLQxJSM9mw6XC4ySGN3LG3FCPoNg04tKy0VGAk7khtR3NQAFb/O4TW0R3r4pi66b8LkvD1LssPdKr3yAO7dnBgd1/8XLXFrTu0IXkpCQO7tlOZmYGC35YhqWVdVmFXOrWrl7BmdOnALjhr3pYs3bNCk76qOabf6lPP17qo3qX0rwFi3hn0gQGDehNtx69qF6jJtEPH7Jr518kJyUx7o1J1K5TvicwEaVLT0+PJb8up0+v7nTp2JbBQ4ZhbmHB9r+2EhoSwuw586hWvbq2w9SK56VuKkTy8KjFYfXq1QwaNIj169eX68QBICJMdWOTkpLCN18tKLKMm5tHQfJgamrKzn2HWTh/Djv+2sopn+M4ODrx5uR3+eiTmWovj6vIdHV1WbJ2KxtWLmH/zq3s3f4HOdlZ2Ng50Kv/IF576z2q1fAGVDOcDBkznmuXLnDi0F6SkxIxNjGlag1vXhowhJeHjSn350JRbE1VT9WN9HXp5a2eOADEpqrm649Ly2LvzWiq25tSx9kcUwNdsnPzeJiSxe4b0RwOjCU1S33w895bMcSmZdO5mi3tvVRPjINj0tjh/5CweM1eDKYtO89HYGGsTxMvW1pWt8fYQI/41CzOBEaz5VQoW8+Gq20zYYkv5wNjGNGuCmM6ViUrO5dzQTF8v+sGpwPUu1nsvRRF3wVHmNbXmx4NXTA20OXOg2Q+3+zH4v0B5JXTrjm2Jo/PnaISB4CY1CwiEjKITc1i981oatibUtfZHBMDXXJy83iQksXOGw85dFv93DlwO5ZGLhZ42hhTz9kcHQUkZuRwLiKRI0Gx3ImtuH39r16+QOCtG9Rt2ITqteoUW06hULBo8WoarFzK1k1r2LJ+JQYGqu5ME95+n6Yt2xa7bUV05vQpNm4oPGPdWd/TnPU9DYCbuzsv9ekHwKgxr+Hm5s6SX37kwrmzHNy/FxNTU+rUrcfoMa8zdMTTxyCKF0P7Dh05fOwk8+bM4s8/tpCVlUUt79rMnD2XYcNHaDs8rXoe6kahVJbTK+QTZs+ezeeff46ZmRnvvvtuke906N+/Pw0aNNBof0n5o9lD78U9NzMYlaSI2DRth1Bu/XJG/aZVqPy5+5q2QyjX+vcq/mb1RTetjae2Qyi3KlkZaTuEcuvJLoqiMJ2i+h4K8RRJSUk42lqSmJj4zHvjCtHyEBoaCqie4n/xhXr3HwAPDw+NkwchhBBCCCHEP1chkofVq1ezevVqbYchhBBCCCHEC03a/IQQQgghhBAakeRBCCGEEEIIoRFJHoQQQgghhBAakeRBCCGEEEIIoRFJHoQQQgghhBAakeRBCCGEEEIIoRFJHoQQQgghhBAakeRBCCGEEEIIoRFJHoQQQgghhBAakeRBCCGEEEIIoRFJHoQQQgghhBAakeRBCCGEEEIIoRFJHoQQQgghhBAakeRBCCGEEEIIoRFJHoQQQgghhBAakeRBCCGEEEIIoRFJHoQQQgghhBAakeRBCCGEEEIIoRFJHoQQQgghhBAakeRBCCGEEEIIoRFJHoQQQgghhBAakeRBCCGEEEIIoRFJHoQQQgghhBAakeRBCCGEEEIIoRFJHoQQQgghhBAakeRBCCGEEEIIoRFJHoQQQgghhBAa0dN2ANpkbKCLsYGutsMod6o4mGo7hHLrzeZu2g6h3LqfkKbtEMq1Az53tB1CueVubaTtEMqtAbWctB1CueVqa6ztEMotPXk2/FS6Ogpth1ChydklhBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IgkD0IIIYQQQgiNSPIghBBCCCGE0IietgN4UV04f555c2Zx9owvWVlZ1PKuzeR3pjB02HBth1YmNv1vPadPncTv8iX8r18jKyuLJb+tYOTosUWWT0pKYv68z9mxbSsPHtzH0dGJvgNe5pNPZ2FhYVG2wZeyvLw8tqxdxvYt6wkNvo2unh41atdj1Pi36dC1l1r5lOQkln63gMP7dhAb/QBbe0c69+jLxKkfY2ZeMevGxkSf1lVsaOxqSWUrI6yM9UnJzOXmg2S2XbnP7ejUIrdzMDdgUINKNHCxwNpEn9TMXCIS0tlz4yGnQ+Kf+pmfda9GEzcrsnLyGLTqYmkcVomwMNbjvV41qeduhautCZbG+sSnZhH8MIW1J0LZe+XeU7d3tTXhwMcdMDXUY/3JUD7ZfPWZn7lqYnM613YkIzuX6tN2l9ShlIorh7YTdv08dwP9eRAaQG52NgPe/5KG3QYWWT7iph8+m5YS7n+JzPQUrBxcqNPhJdoNnYi+oZFa+ezMDM7t/B9Xj2wn/n4kKJVYOrpQp11PmvcbhZGpeWkfYolTKpUc3reD/636lZDg26QkJeFUyYUmLdvy2ptTcXX3BCA7O5tjB/dw7OAervld4H5UJAodHbyq1aTvK8MZNPI1dHV1tXw0JWvzxg345l+rbvirrlW//LqCEaPGqJVdMO9zFs6fW+R+DA0NeRBf9O/W82zH9m0sW7qEK36XSEtLw9HRiabNWzBv/kIqu7pqOzyt2LhhPadO+nD50kWu59///LZ8FaPGjNV2aBqT5EELThw/Rp9e3TEwMGDQ4KFYWFqy/a+tvDp6BOFhoXw4/RNth1jq5s6eSXh4GLZ2djg5ORMeHlZs2dTUVHp27cjVK3506tyFV4YM5frVK/zy4/f4HD/GgSMnMDU1LcPoS49SqeTDSWM4vHc7ru6e9BsyiuysLI4d3M3UcUP56PNFDB37RkH59LRUxg3uRcCNq7Ro25EefV/h9o1rbFjxCxd8fVj1536MTSpe3fSu7cjABs7cS8zALyqJxPQcKlkY0tzDmubu1nxzNJhTdwonA/VdLPika1UAzocncD8pEzNDPTxsjKnvYvHU5KFzdTsaVrYkMycPRake2X9nbWrI4JZuXA6N58DV+ySkZWFrZkiXOk78Oq4p/zsVyvRNxScEX49o8I8+b3ALVzrUciAjK5dyXznA4TXfkfAgChNLa8xtHEh4EFVs2Rsn97Pliyno6Ojg3aY7Zjb2hPtf5PiGXwjxO8PYhWvQMzAsKJ+bk82qD0YSeesKTl61aNB1AAoUhFw5w+HV33Ht6C4m/PQnBkbGZXGoJebruZ+wdtnP2Ds40albb0zNzbl94zp//m81e7f/wbpth6hW05uIsDtMe2MkpmbmNGvVjg5de5GSnMTxg3v54tNpnDp2kB9XbkahqAAniobmfT6TiPxrlaOTMxFPuVY9MmzkaNzc3Ast09N7sW63lEol77w1kVUrllGlihcDBw3B3Nyce/fucdLnOOHhYS9s8jB71qeEh4VhZ2eHk7Mz4WHPPqfKmwpxNickJDBz5kzOnz9PSEgI8fHx2NnZUaNGDd566y1efvnlCvNjlZOTw5tvjEOhUHDwyAkaNGwIwIzPZtGhbUvmfj6LlwcOomq1alqOtHT9vOQ3vKpWw83dnW8WLWT2Z8UnTN9/s4irV/yY8t4HzP3iy4LlX8yZzZfz5/L9N4uYMXN26QddBg7t2c7hvdtp0KQFSzZsxyj/JmTyh7MY0ac9383/lHade1DJVXVhWr30ewJuXGXsxCm8+/Gcgv0s+fYLfvthIauXfs+b02Zo5Vj+i9vRqXy88yY37qcUWu7tZMacXjWY2NqDs6EJ5OQpAbAzNeCjLl7EpmUzc3cAMalZhbbTecrPg62pPq+1cGXn9Qe09LTG2li/xI+nJEXEplLnw73k5h/7I6aG19n+XluGt/Zg5bEQbt9PVtv21faeNKliw/ztN5j1cp1nfpaTlRGfDajDimN36FnfGXsLw2duo239pn6BrYsHVo4unNj0K4dWfl1kuezMDHb88BkKhYJx322mUnVVfSiVSnb/ModzO9Zzeutq2g19nKzfPHWQyFtX8G7TjaEzfym0v42zJ3Hz9EFu+OyjQdcBpXeAJSzm4QPWr1iMi6s7f+w/Xai1ct3yX/jq8+msXfYTc79ZgqmpOZ9+8R19Bw3H2NikoFzaZ/N5bVAvjh3ay4Hdf9G9d8U5/mf5afGvVKlaDTc3d777eiGfz3z27+nwkaNp265D6QdXji355SdWrVjGhImTWPTtD2otUjk5OVqKTPuW/Locr6rVcHd3Z9FXXzJzxsfaDukfqxBjHmJiYli5ciWmpqb079+f9957j549e+Lv788rr7zCG2+88eydlBPHjh7hTnAwQ4YOL0gcAMzNzZn+yWfk5OSwds0qLUZYNjp27oKbu/szyymVStasXoGZmRnTP/ms0Lr3PpyOtbU1a9esRKlUFrOHiuXYgV0AvPbWewWJA4C1jS0jX59EVmYm239fD6jqZtumtZiYmjHh3Y8K7ee1Se9hYWnFX5vXVci6ORMar5Y4ANy4n8L1e8lYGKlaFB55pYEzpgZ6LDkZqpY4AOQ9pQrebudJUkYOGy5ElkjspS1PiVriAJCamcvxm9EAuNurtza525nyUZ9aLD0UhH9EokaftWh4A+JSMlm06+Z/C7oMeTVqjZWjyzPLhftfJC0xnpqtuhQkDgAKhYLOY6cCcGH3xkLfn7h7EQBUa9pebX/VmqmWpSTE/qf4y1pUZBh5eXk0aNJCrZtju87dAYiLVZ1Xjs6VGDJ6XKHEAcDExJTR4ycDcOHMyTKIuux06NRFrRVBPF16ejpffjEHT88qfPXN90V2ZXvRWmKe1KlzF9w1uP8pzyrE/z1PT08SEhLUTrbk5GRatGjBsmXLePfdd6ldu7aWItTciePHAOjctZvaui75y3xOHC/LkMq1oKBA7t29S5eu3dS6JhkZGdGqTVt279xBcHAQVatW/Naa2BjVRdrF1UNtXaX8ZedPn+DNaTMIDwki+sE9WrXvrNY1ydDIiEbNW3PswG7CQ4Nx96xa2qGXmUetDU/eQLeuYkNSRjbX7ibjZWdCbWdzdFAQEpvG1btJFJc7dKtpT30XCz7dFUBWbsVLsp5kqKdDq+p25OUpCfxbq4NCAd+MbEBUXDo/7LtNIw/rZ+5veCt32tawZ8hPp8nMziutsLUmJT4GAGunymrrjM0sMDa3JOFBFPH3wrGppLrQO7irvkeB50/QuOfgQtsEnj+OQqHAs37zUo68ZLl7eqFvYIDfhTOkpiRjavZ4zIbPkQMANGulniz9nZ6+qsXuRb4pfMT31EkuXTiPjq4u1avXoEOnLhgalv9Wu5Jy5NBB4uLiGDFqDLm5uezetYOgwNtYWlrRsVMXvKo+P9ejF1WF+JYXNwDL3Nyc7t27c+PGDYKCgipE8hAUFAhQ5I2utbU1dnZ2BOeXERTUhVcxicGj5cFBgc9F8mBtYwtAVEQoVarVKLTubkQoAGEhQQCEhwQD4OrhVeS+3PKXh4c8P8mDnakB9StZEJeWRVh8OgCO5oZYGOlx+2EKE1u709PbodA2wTGpfHEgkNjU7ELL7c0MeLW5K/tuRuNfRBef8s7CWI/XO3ihUICduSEdvR1wsTHhuz0BhP5tQPnrHarQ2NOGgd+dJCvn2YmAi7UxM/p7s/5UKGeDKtaTdE2ZWtoAqAY9/01GajLpyarWmZjI0ILkoUaLTtRo0YkbJ/ezZFJ/POo1AyD06lni7obR++3ZuFSvW0ZHUDKsrG1554OZfPPFp/Tt2ISOXXthYmZG4K0bnDl5lFdGvMrwVyc+cz/bNq8DoGW7TqUdcrk3f+7sQn87OTmzZNlKOnbuqp2AytilSxcAVSLZokkDAm8HFKzT0dFh8jtTmL+w6O6EomKoEN2WipORkcGRI0dQKBR4e3trOxyNJCWqLkiWlpZFrje3sCAxUbMuBS+CR/VlYVF0fVnkN7MnPSd11rqj6uKyasl3ZGZkFCxPiI9lw4olACQnqY41JTkJAPNiZlR69ATxUbmKTlehYGpHTwz0dFhzNrKgK5KlseoZiJedKR2q2vLDsTsMX3OJ8RuvsP/mQ7zsTPmos3ry9HY7D1Iyc1h7LqIsD6PEWBjrM7VXDab0rMHINh7YWxgxb5s/3+0NKFTO096UD3rXZOWxO1wKffqMU48sGtGAxPRsFmy/URqhlwuutRthaGLGrdOHuBfkX2jd4dXfFfx3Rurj749CoWDYrMW0fuV17gffwHfrKny3ruJe0A1qtupK1Sbtyiz+kjR24rt8+eMKUpKT2LxuOauWfM/JoweoU78RvQcMQV//6eOAft+wkpNHD9C8dXvadepeRlGXP3XrNWDJslVcvRXM/bgULl27xYyZn5OYmMCwQQO4dvWKtkMsE9EPHwLw4/ffYmFhwfFTZ7kfm8T+w8epWq06P37/Lct+XaLlKMV/USFaHh5JSEjg+++/Jy8vj4cPH7Jnzx4iIiKYNWsW1Z4ywDgzM5PMzMyCv5OSno+bKfH86dF3EDu2bOC87wkGdW9Bq/ZdyMnJ4diBXdjYqZ6o6+o8X1MhakIBvN3egzrOFuy/+ZBjTzwN18mfAkhXR8GGi1EcCVStS83KZfHJMDxsTKjhaEYtRzNuPlCNo+hZy576LpbM3htAegXtkhMZl47b2zvQUUAla2P6Nnbhg941aVzFhkkrL5Cbp8zvrtSQB4mZLNp1S6P9jmrrQZsa9oxa7EtqZm4pH4X2GBqb0uONT9j+3Scse3cw3m17YGZjT8SNS9wNvI6daxViIu6g88T3LTszg9/nTyHy1hVemf4tXo1bAwru+PmyZ/FcAs8fZ8IPvxe0VFQUv/7wFUt/+JJJUz+mzyvDsbC0IsD/KovmfMLrQ17i6yVr6NKzX5Hbnji8j/mfvU+lym4s+GF5GUdevvTuW7iOqnhV5YPpM3BwcOTdyRP5euF81mzYrKXoyk5enuo31cDAgE2/b8O5UiUAWrdpy/pNv9OicX1++v5bxr/xpjbDFP9BhWp5SEhI4PPPP2fu3Ln8+uuv3L9/n0WLFjFr1qynbrdgwQIsLS0L/rlqcXowi/wWh+JaF5KTkoptlXgRPaqvpKSi6ysp/6m6xXNSZ3p6evy85k8mTv0YHR0dtm5czZF9O+jQ9SUWLVkLgJWtHUDB4MbkYloWUlOSC5WryN5q50HHanYcDYxhycnC09qlZj+eteNcWILatufDVcuq5g8itjHRZ0wzVw4HRHM5suI/SMhTqhKJxQeD+Hr3LXrWd2ZYK9XN62vtq9DIw5oP/+dHRvazEwFHSyM+7uvNljPhBYOvn2eNew5i5LzlVPZuyC3fw5zfuQEdHV3GLlxbkACY5HdvAjixaSm3fA/Td8o86nbsjYmFNSYWVtRp15MB731JWmI8x9b/UtzHlUtnTx3n56/nMmzMBMa//QFOzi6YmJjSsGlLfl69BUMjY776vOjZYE4dO8TUN0Zia+fA8k27sHd0KuPoK4ZhI0ejp6fHWd/T2g6lTDy6h2nYuElB4vCIt3dtPD2rcOdOMAkJCVqITpSECtXy4OHhgVKpJDc3l4iICDZt2sSMGTM4ffo0W7ZsKXag1scff8y0adMK/k5KStJaAvGoX35QUCCNGjcutC4+Pp6YmBhatGyljdDKpSfHNBTlWWMiKiIDQ0PemPIxb0wpfMG+4OsDgHdd1Sxdbp6qMQ0RocFF7ic8f/mjchWRApjczoMuNew5HhTLj8dD1AY/30vMJDdPia6OgtQs9en/UrNUN80GuqpnJZUsjTA20KVzDXs617Av8nO3j28KwPA1lwq2rwhO3IpmBtCymi3rT4biXdkCHR0FW95tXWT5kW08GNnGg/1X7zF+2Xk87U0xM9JjcAs3BrdwK3Kb8J/6AlDnwz0kpVf86RarN2tP9WbqA4L/XPgBCh0dKlV7PJbu9tljAEUOivas3xyFQsHdwOulFmtpOHF4HwDNWql3ubKxtadaTW+uXDxHfFwM1jZ2BetOHj3IlPHDsbK2ZeXm3QUvkhPqDAwMMDM3Jy09TduhlIlq1VXj9awsrYpcb2mlWp6Rng5WRZcR5VuFSh4e0dXVxcPDg+nTp6Orq8uHH37IsmXLePPNopvADA0Ny81MB23btWfRwgUcPniAwUOGFlp36OCBgjJCpWrVajhXqsQZ39OkpqYWmnEpIyOD0yd9cK5UCS+v52NA8NPs+WsLAD36qt6U6+ZZFXtHZ/wunCU9LbXQjEuZGRlcOnsKe0fngoHTFc2TiYNPcCzfH7tT5JSrOXlKbj1IobazOa5WxgVdkx5xtVK9JfhhiqrrYnxaNgdvFf1UvU0VGwz0dDhyWzUTT3ZuxerS5GipOtac/JmjzgTGFvz3kxwsjehc25HA+8lcuBOHf6SqZe9hUgYbTxf9wqI+jVww0tfh97OqMSKZGgy8rqjC/C+S8CCSas3aF3pjdG6OatB9amIchiZmhbZJS4pHqVSiq29QprH+V9nZqmOKj40pcn18rKoboMETL8s7efQg744fhqWlNSu37K7QDyjKQnBQIAnx8dSpW1/boZSJdu07AhBwS32K5+zsbO4EB2FqaoqdfdEPb0T5V6G6LRWlWzfV9KbHjh3TbiAa6tipM55VqrB50/+44udXsDw5OZkv589FT0+PUaPHai2+8kahUDBm7OukpKTw5fy5hdZ989WXxMfHM2bs6xXmJYGaKGqA88Hdf7F9yzpq129Epx6qJ78KhYIBQ0eTlprCbz8sLFR+5eJvSEpMYMDQ0RWybhSoBjR3qWHPyTtxfHu06MThkb03VQP0hjWuhN4Tb4RzsTSiU3U70rJyuZT/boOoxAx+9gkt8l9SZg65ecqCv8vj9K3eLhaYG6k/97E00efD3rUAOHZDVR+/n43go41X1P79ekg1Y9fZoFg+2niFtT6hANx5mFpk+Y82XiE+NYucPGXB38/D9K0ZqeqzbCXFPmD7t5+go6tH5zFTC61zq90IgGPrfiYv93GLVF5eHkfW/ACAZ4MWpRhxyWvYRBXv2mU/F0zG8Mj23zcQHhqMd92GBRMwPEocLCytWLFl93Mzk9t/lZyczPVr6m92T4iPZ/Kb4wF4ZfCQsg5LK6p4edG5SzeCg4NYvbLwOJhvFn1JQkICffoNkGl9K7AK/3/u7t27QMWZW1pPT48lvy6nT6/udOnYlsFDhmFuYcH2v7YSGhLC7DnzqFa9urbDLHWrVy7H9/QpAG74q5r516xaWfCOi959+9Gnb38Aprz3AXt271S9adrvMg0aNub6tSsc2L+PevUbMOW9D7RyDKVldL9OOFZywbNqDQwNjbjud5ELZ3yo7ObBV4vXFpq6eOzEKRw/uJfVS7/nlv8VatVtyO0b1zh17CA1vOsxduIU7R3IfzCkUSU617AnPSuXu4kZDG5YSa3M2dB4QuJU07X6BMfR0sOa1lVs+GFgbS5HJmFioEsrD2v0dXX44didCtX96GkGNXdlaCt3fG/HEBmfTnpmDi42JnSq7YiZkR57Lt/lr4sV44V3peHi3i2EXVdNFfkg5Hb+st8JuXIWgFqtulKrtWpWszN/reXq4R241WmMqZUtSdH3uOV7iOyMDPpNm1+oyxJA+2GTCPA9gt+hbdwNvI5ngxYoFApCrpzlQUgAVo6VaTt4fBke7X/XrfcAft+wkvO+PvRu14AOXXupBkzfuI6vzxEMDA35aPaXANwJCuDd8cPIysykaYu27N3+h9r+KlV2o//gkWV9GKVm7aoV+PrmX6uuq65V61av4KSP6lr1Uu9+9O7bj7i4WNo0b0TDRk3wrl0Hewd77t69y6ED+4iLjaVj5y5MenuKtg6jzH334y907tCayW9OYNeO7VSvUYMrfn4cP3YEN3d3vljwlbZD1JpVK5Zz+pTqZYr+16+plq1cXvAesD79+tO3X38tRaeZCnHH7efnh6enp9pA4ri4OD755BMAevbsqY3Q/pX2HTpy+NhJ5s2ZxZ9/bCErK4ta3rWZOXsuw4aP0HZ4ZcL39Cn+t35toWVnfE9xJv9H2t3doyB5MDU1Zc+BIyz4Yg7bt/6Jz4njODo68dY7U/h4xky1l8dVdN36vMyRfTu5dvkCOdnZVHJ1Z9zbHzDmjXfVBj8bm5iybPNufv3+Sw7v3c6FMyexs3dkxOtv8caU6Wovj6soHMxVXSSMDXSLTBwAHiZnFiQPAF8fCebWgxS61LCje017svPyuPUwhd8v36uQ73Eozh6/e5gb69PQw5pmVW0xNtAlITWb83di+fNcJDsuRmk7RK0Ku34Bv4PbCi0L979IuP9FAKwcKxckD27ejQi9eo6AM0fISEnC2MKKak070HbIeJyrqr83yNLBmYmL/+LExiUEnj/BhT2bUKDA0tGFVgNfo92wiZhYPPsFfOWJrq4uS9dtY/3KxezbuZW92/8gOzsLGzsHevUfzLi33qNaTdVU6LHRD8nKn7lw7w71xAGgSYs2z1Xy4Ot7io1q16rTnMkf/Ozm5k7vvv2wtrZh/BuTOH/uLPv27iIxIQETU1Nq167D4KEjGP3q68W+s+p5VMXLC5/T55k3ZxYHD+zj8KEDODo5MWHiJKbPmImDg8Ozd/KcOn3qJOvXrSm0zPf0qYIHqu4eHuU+eVAolcry1y7/N1OmTGH58uV07NgRd3d3TE1NCQsLY/fu3aSkpDBw4EC2bNmCjo5mvbCS8mc0ehCbiIVFxZ+JpqTlVLB+3mUp6EHqswu9oGbt12wq0BfV5av3tB1CuTW+b8V4T482DKglMxgVx9XWWNshlFt6uhW+V3qp0tWpeN15S1tSUhKOtpYkJj773rhCtDy88sorJCYmcubMGU6cOEFaWho2Nja0adOG0aNHM3To0ArZr1sIIYQQQoiKpEIkD23atKFNmzbaDkMIIYQQQogXmrRrCSGEEEIIITQiyYMQQgghhBBCI5I8CCGEEEIIITQiyYMQQgghhBBCI5I8CCGEEEIIITQiyYMQQgghhBBCI5I8CCGEEEIIITQiyYMQQgghhBBCI5I8CCGEEEIIITQiyYMQQgghhBBCI5I8CCGEEEIIITQiyYMQQgghhBBCI5I8CCGEEEIIITQiyYMQQgghhBBCI5I8CCGEEEIIITQiyYMQQgghhBBCI5I8CCGEEEIIITQiyYMQQgghhBBCI5I8CCGEEEIIITQiyYMQQgghhBBCI5I8CCGEEEIIITQiyYMQQgghhBBCI5I8CCGEEEIIITQiyYMQQgghhBBCI5I8CCGEEEIIITQiyYMQQgghhBBCI5I8CCGEEEIIITSip+0AtEmpVKJUKrUdRrmjq6PQdgjllqutsbZDKLemtfPSdgjl2mfJmdoOody6eT9F2yGUWx3ds7UdQrmVlZOn7RDKLXPjF/r27plszQy0HUK5k5aZo3FZaXkQQgghhBBCaESSByGEEEIIIYRGJHkQQgghhBBCaESSByGEEEIIIYRGJHkQQgghhBBCaESSByGEEEIIIYRGJHkQQgghhBBCaESSByGEEEIIIYRGJHkQQgghhBBCaESSByGEEEIIIYRGJHkQQgghhBBCaESSByGEEEIIIYRG9DQp1KlTpxL5MIVCweHDh0tkX0IIIYQQQoiypVHycOzYsRL5MIVCUSL7EUIIIYQQQpQ9jZKHWbNmlXYcQgghhBBCiHJOkgchhBBCCCGERmTAtBBCCCGEEEIjkjwIIYQQQgghNKJRt6Vn2bFjB/v37ycsLIz09PRCMyqlpqZy5coVFAoFLVu2LImPE0IIIYQQQmjBf0oeIiIiePnll7l06RIASqVSbUYlQ0NDhg0bRmRkJH5+ftStW/e/fKQQQgghhBBCS/51t6W0tDS6devGxYsXcXFx4a233sLU1FStnJ6eHuPGjUOpVLJ9+/b/FKwQQgghhBBCe/518vDLL78QEBBAo0aNuHnzJj/++CNmZmZFlu3Xrx8ABw4c+LcfJ4QQQgghhNCyf508/PHHHygUCr799tsiWxyeVKdOHfT09Lh9+/a//TghhBBCCCGElv3r5CEgIABdXV1at2797A/R0cHS0pL4+Ph/+3FCCCGEEEIILfvXyUNmZibGxsbo6upqVD41NRVDQ8N/+3FCCCGEEEIILfvXyYODgwMpKSkkJCQ8s+yVK1fIyMigcuXK//bjhBBCCCGEEFr2r6dqbdWqFVu2bGHLli1MmDDhqWW/+OILFAoF7du3/7cf99xISEhg7uczuXjhAqGhISTEx2NrZ0f16jV4Y+Ik+g14WW262xeJ1A/cuxvF9m1/cGj/PgJvB/DwwX2srW1o1qIVb099j8ZNmz91+7DQENo1b0hqaipjXhvPNz8uLqPIS86BHVu4duEMAf5+hNy+SXZ2Fh/N/4meLw9XKxt48xrH9v7Fbf8r3L5xlcT4WOo3bc0P63YUu//kxATWLf2Wk4f3EH0vChMzc+o3bcWrb0/Hs1rN0jy0/8zOzICO1exo4WmNm40xNqYGJGXkcP1uEv87H8nN+ylq21S2MmJkM1fquVhgb64qHxqbxtbL9zh1J06tvIGeDv3rOdGtlgPOlkYAPEjO5GhANFv97pGalVvqx/lvWBvr09zdigYu5lSyNMLKSI+UrFxuP0xl542HBMekPXV7ezMDFvaugZG+Lodux7DybKRaGX1dBV2r29Ha0xoHMwNQKIhJyeJMWAIHAqJJz84rrcP7z/Zv38LVi74EXL/Cnds3yM7O4uMFP9OrmO/Vkb1/EeDvx21/1feqQbPW/LRu51M/Izsriz83LOPQzj8JDwkCwMmlMg2atmbarEWlclylKS8vj9/XLWf77+sIDQ5ET0+PGt71GDluMu279ipym6iIUFb+8g1nfI4SG/MAc3NLPKvVZNDI1+n60oAyPoKycWDPDv63+jduXLtCenoa9vaO1G/clA8/+wJnF9WD483rV3Fk/24Cb90gNiYaXT09XFzd6NK9N2MmvIWVtY2Wj6Lk3L0bxY5tf3CwmOt4kyeu49nZ2ezdvZP9e3dx8fx5oiLDUejoUKNmLYYOH8XY1ydo3MunrPzr5GHixIls3ryZ2bNn06ZNG7y9vdXKpKWl8cEHHxQMrp44ceJ/CvZ5EBsTw9rVq2jWvAV9+vbD2tqG6OiH7N29i+FDB/Hq6+P4Zclv2g5Ta6R+YNnSX/jx20V4VvGiQ6fO2Nk7cCcoiD27trNn13Z+W7We/gMHFbmtUqnk7Ynjyjjikrfi+/k8uBuBpbUtNvaOPLgbUWzZk4f2sOG379HXN6CyhxeJ8bFP3XdifBxvDe1BZFgwtRs0pU2nnsRGP+DEgZ2c8znMt6u34V2/SUkfUokZ2MCZEc1ciUxI50J4AvFp2VS2MqZNVVvaeNkyZ08AR2/HFJSv5WTGD4Pqoqej4NSdOI4HxmBlYkC7qrYs6G/NytNhrD7zuH51dRT8MKgOtZ0tuP0whX03HqBUQkNXS8a38aBzTXve+N8VMnPK301y95p29K3jyP2kTK7dSyYpIwcnc0OauFrSxNWSn06GcTYsodjt32jp9tT96yrg065VqWZvSmhcGifuxKNEibejOUMaOtPK04rP9twmK1dZwkdWMpb/8AX3o1TfK1sHR+5HFf+98jm0m/W/foe+vgGuns/+XoEqKX9v3CvcvHqJOg2b0W/oGADuRYZxZM+2Cpc8KJVKpr81hsP7dlDZ3ZP+g0eRlZXJ8YN7mDZhGB/OXsSQMYUfnp7xOcJ7b4wAoF3nHri4eZCUmEDQLX/OnTr23CUPSqWSmR++w+Z1K3HzqEKvfq9gambGw/v3OO97kqjI8ILkYfsfG0lKiKdx81Y4ODqRlZWJ38Xz/PLdl2zbsoHf9x7D3sFJy0dUMp51HV/2xHU85E4wr44cgpm5OW3bd6THS71JTkxk397dfDjtHY4cOsD6zVvL1YPTf508tG/fntdff50VK1bQvHlzXnrpJVJTUwFYtGgR165dY/fu3QXdmqZMmUL9+vVLJGiAr776io8++ggAX19fWrRoUWL7Lk0enp7ci45HT69w1ScnJ9OhTUtWrVjOW5Pfxbt2bS1FqF1SP9CocVN27j9Ky9ZtCi33PXWSl3t344Opk+nZu2+RY4iWLfmZc2dOM2veAj6b/kFZhVziPpj3PZXdvXBycWXDb9+z7Nu5xZbt0KMfrTv1oEp1bxIT4hjYVv1BxpNW/fQlkWHBDB47iUnTH+/X//J53h75El/NeIeVO06io/Ove3WWqpv3U5i8+SpXo5IKLa/nYsF3r9RhWmcvTgbHkp1/A/tqSzeM9HX5+K8bhVoZVvmGs3p0Q4Y3rcyG85EF5dtVtaW2swXHA2P4bOetQp8xr28t2lW1pUM1O/bffFjKR/rPBcek8fn+QAIephZaXsPBlBldvHiteWUuRiSSk6d+c9+9ph3VHUzZeOkuo5q4FLn/Jm5WVLM35VxYAt+fCC20bmp7D5q6WdHc3QqfO+VzcpCP5v1Q8L1a/9v3/PrNnGLLduzRj9adeuKV/73q36bWM/f/5Yx3uHXtMjO//o2ufV4ptC4nJ+c/x1/WDu/dzuF9O6jfpAWL1/2FkZExAG99MItRfTvw/fxPadu5O5UquwNw/24kH741BgdHZxav346zi2uh/VXEOniWdSuWsHndSka8+gafzluk9oT8yWNetWkHhkZGavv4fuEcFn+3kJVLfuSjWfNLPeay0KhxU3YVcx0f0Lsb7z9xHTczN2fRdz8xdMRoTExMCsrOSU2lb8/O7Nuzix3b/qTfy6/8/WO05j9dHZcuXco777xDWloaW7ZsISVF1Vw+ffp0NmzYUDC70rRp0/j666//e7T5bt68ycyZM585RWx5pKurq3ZjDGBubk6Xbt0ACA4OKuuwyg2pH+jdb4DaDw5Ay9ZtaNOuA/Fxcdzwv6a2/k5wEPNmf8rbU96nbr2GZRFqqWnSqgNOf7vwFsezWk2q166Pnr6+RuVPHt6Djo4OY9/+qNDy2g2b0qpjd0KDArhy7tQ/jrmsnAiKVUscAK5GJXE5IhFLY32q2D3+bXS2NCJPqeRsaOEb2ofJmYTEpGGkr4uJ/uMLfqX8bkpnQtRvgM+EqJIPaxPN6rqsnY9IVEscAAIepnLjQQrmhnq4WqnfvDiaGzCkoTO7/B8QGpde7P4dzAwA8LurXv9++f9PLIz+9TO5UvfPvle1qPEPvlf+Vy5w4uAuuvUdrJY4AEX+rpd3xw7sBuC1Se8VJA4A1ja2DH99EllZmez4fUPB8pWLvyE1OYmPv/hOLXGAilkHT5ORns7P3yzA1d2TGXO/KrJrzZPHXFTiANCjj6o1Jiz0TukEqgV9/sF1vFIlF14bP7FQ4gBgamrKpMlTADh18kSpx/xP/KfkQVdXl++//56rV6/y7rvv0qRJE5ydnXFwcKBevXpMmjSJS5cu8fXXX5dYc0tubi5jxoyhfv36DBjw/DT/ZWRkcOzoURQKBbVqPf3J6YtI6kfl0YX87xehvLw83pk4jsqu7rz/8afaCK3CiI+NxtLaFhNT9ZdaOruoniBeOutT1mGViEdP1HOfeLIeGpuGjkJBU3erQmUdzAzwtDMhODqVxIzHTwdDYlXjApp7WKvtv4WHNXlKJZcjE0sh+tL1qG7+3uigQNVdKSYlm61XHzx1H1GJGQDUr2Shtq6+iwV5SiU3ihhz8iI4smcboGqxSIiLZfcf61n363fs376FxHj1cTUVQVyMqnXNxdVdbZ1LfmvD+dOqmzqlUsmh3duwtLahWav23Lx2mfXLf2btbz9y9uRR8vLKXze//+rUiSMkxMfRpUdv8nJz2b97O7/+9DUb1ywnLCRY4/0cO7QPgOo1X4xru34x1/H/WrYslUg0tWvX5rvvviuJXT3TwoULuXLlCpcuXWLRoorVf/JJCQkJ/Pzj9+Tl5REd/ZD9+/YSGRHBJ5/OpGq1atoOT+ukftRFRoRz4uhhHByd8K5dt9C6pb/8wLmzvuw+eEymRH4GK2tb4mOjSUtNUUsg7kWFARAZqvmFr7xwMDeksZsVsSlZ3Il5/PR9+akw6layYF7fWpwMjiMqIR0rY33aVbPlQVIms3YV7pp0+k4cp4Jj6VDdjmUjGuCXnyg0rGyJi5UR3x4OJuBBxbpBtjXRp46zOfFp2YQnFG5Z6FnLnur2pny+P7DI7kxPuhSZxMWIRJq7W/FFr+rczK+HWo5mOJobsupsJCFPabl4ngVc9wMgMvwO8z6cSEry49YZYxMzPvriezr3ellL0f07VrZ2AERFhOFZtUahdVGRqt+KR4PCoyJCSUyIp3a9Rsz/dCp/blhZqHyN2vX4btkmHJ2L7hJXEV33uwSobmz7dG5BSNDjFwHr6OgwdsJkps9eoLbd1k3riIwIJzUlmRvXrnD29Am869bn1TfeLrPYtSUyIpzjRw/jWMR1vCgb1q0GoGPnrqUc2T9TvlKZZ7h+/Tqff/45n376KbUreJ/3xIQE5s973N9UX1+f+V9+xbtT39NiVOWH1E9h2dnZvDluLJmZmcyet6BQ83BQ4G0WzJnFhElv07R5Sy1GWTE0b9eFPX9uYM0vi3jzw88Llt+8ehHfYwcASEmqWE/WdXUUfNqzOoZ6OizyCSn0dD0sLp03N17h89416VjdrmB5UkY2e/wfEJmgfrM7Y8dN3mjrwZDGLtRwfJxg7bvxkHOh5bM/f3F0FTCpjTsGujosvxSB8om6cTI3ZFADZ/bdiibwGTMxPfLt8RCGNaxEL297PG0fdzPwCY7jyt3kkg6/woiPVQ3SX/LVLLr2GcSrkz/E3NIa32MH+Pbz95n34Zu4V6lB1ZoV59rdun1X9u/4g9VLvqNpq3YYGqq63STEx7FxpWoWu+Rk1W/Fo+O/5X+FkODbzFq0mA5de5GSnMTKX75h26Y1fDhpNGu2HdbOwZSC2NhoAFYu/RHvug34Y+8JvKrX4Ma1K3z2wdusXPojbh5VGD52fKHttm7ewDnfx627bTp0ZtFPy7G0Um/tfJ48eR2f9bfreFHWrFzGoQP7aNu+I1279yyjKDVToslDWFgYDx+qmvkcHBxwd1dv6vu3cnJyGDt2LLVq1WL69On/aNvMzEwyMzML/k5KUu+vWtbcPTxIy8ojNzeXyIgIft+yidkzP+WMry/rN24ud01UZU3q57G8vDzeeXMcvqd8GDX2dQYPG1lo3dsTX8fJuRKfzCx+8KN4bOzbH3H2xCE2r/wZf7/zeNdvQlz0A47v34GHVw2CA/zRKWfT4j2NApjerRoNKluy4+p9DtyMLrS+hqMZ8/vVIjQmjXHrLxMWl46NiT79Gzjzdocq1Hex4NMnBkYb6Okwu1cNajmbM2dPABfCElACjV0tebejFy08rJm48Qp387vwlGcKYEIrN2o5mnEkMIaTT4zjUAATW7kRn5bNFr97Gu1PX1fB2209qGpnwi8nw7h2NxklUMfJjNFNK1PfxYKZe2/zMCWrVI6nPMtTqrrlVKlRm0++/KWgq3K3voNIS03hm9nv8ce6X5n+xY/aDPMf6d73FXb8sZ4Lvj4M6dGSVu26kJOTzbEDu7GxcwBAR0f1W/GoW1Jubi5vTptB31dUMy5ZWFrz6YIfCbzlz3W/C1w+70vDps/HQ55Hx6xvYMDi1ZtxdHIGoGmL1vy0bD19OjVn5dIf1ZKH9dtU3ZTiYmO4cukCi+Z+Sv9urVm24f/s3Xd0FGUXwOHfppPeA6QHQq/Sey/SBakCgiCoCNYPRaUKoiCooDRBOqIovfcSCL33JKRTQnqvu98fgWDcDSwxyW7gPufkSGbemb0zbmbmzts2UqXas9/Gl0ZKpZKx747kxPFjDB02gv7/uI9rsnf3Tj775APcPTxZtGxlCUWpvf88nMjdu3cZO3Yszs7O+Pj40LhxYxo3boyPjw9OTk6MHTuWiAj1sbKf1zfffMOlS5f47bff8tqAaWvmzJnY2Njk/bi7a9dhrCQYGhri6eXFp+M/Z/LUr9m6ZRO/LftV12HpjZf9/KhUKj56fzQb1q+j74BBanM2LFk4n7OnT/HDz4vVOlsJzZzLurLor/10eX0w9yNC2bhmCdcvnWX4uM95Y/RHQG7TptJifMeKdKrmzJ7rUczZn38wAUMDBVO6Vkalgi+23uB2VAoZ2UruJWaw8GgIB24+pKWvI3XdbfK2GdzQjeYVHfh+XyAHb0WTmJ5NUno2hwNimLn3NrbmxgxrrD/X0KcZ2cSdFj72HLsTy7KT+e9Dnao4UdHJnF9Phms9tGrPGi7Ud7dh6clw/EPiSc7MISUzh1NhCSz2D8PazIjetV6MoSafl6Vlbj+Qpq07qvVxbNa2E/CkaVNpYWRkxPzlfzP6wwkYKAzYuH4FB/dso1WHLsxasAoAO/vc2jxLqyf9YFq2V39L3LJdZwBuXLlQApGXDKtHx1yj9it5icNjvlWq4e7pTVjIHRIT4jVub+/gSJsOnVn2+2biYmP46pP3iztknVCpVHz4lPv4vx3cv5dhb/TDydmFzTv2UvZf51Yf/KfkYe/evVSvXp0FCxYQHR2NSqXK9xMTE8OCBQuoUaMGu3fvLvTnXLp0ienTp/Ppp5/yyiuvPPf2EyZMICEhIe8nPLzgsa11qV373NGEjh05ouNI9NPLdn6USiUfvPc2a1etoHffAfy8+De14UOvXr6ESqWiV5f2OFoa5/306tIeyK32dLQ0ZsiAPro4BL3l5FKe8dN/4q+j19h/5T7r9p1j0NsfEBqU22a3co06ug1QCwrgs46+dK1Rln03o5i55zb/fgT2tC+Dq20ZbtxP0jgvw/nweAAqOz9pmtTE2/7ROvWmWxfCE1CqVFRyUe9srk8UwKgm7rSp6MDx4DgWnQjTeG4MFAomdqzIuiF18n4mdqwIQPtKjqwbUoePW3vnbVPXNfdhSVOn6Ov3k1GqVHg7lFFb9zJw9849b5bWNmrrLK1yl2Wk639t1b+ZmJoy6oPP2XjwHCdvPWT/2SC+/OYnoh7cBaBarToAuHv65DVDsdJwDqysbQFIT39x+sR4V6wEgLWG44Un5+FZx1zO1Y0KvpW5cvEcaanaNR8sLZRKJeMe3cf79B3ALxru4/90YN8ehgzog72DI1t27sPL26cEo9Veodt+3Lp1i169epGeno69vT3vvPMObdu2xdU1tzNQZGQkhw4dYvHixURHR9O7d28uXLhA5cqVn7FndW+++SYVKlRgypQphYrV1NS0VHQivXcv92L0MjXJeR4v0/lRKpV8OGYU61avpFeffixcukJj+8imzVtiqOF8PLh/n/17duFbqQoNmzSh1qMbnChYTk4OB3duxNDIiJYdu+s6nKdSAOM7+tK1hgsHbj5kxq7baqMIARgZ5L4Bti2jubb28fLMnCeJhbHho23MjUhLyD+TtE0ZYwwUirw5IfSRAni7iTutKzrgHxLHguOh+fo5PHbzQTJKDSfNtowRdd1siExI53ZUSr6hWw0fnU9rMyPS/9U0ycrUCAOFgmw9PjfFqV7jFuzbtoGQwFtq6x4vK+tWOmqstLFr858AdOyW+2LGxNSUWq804sKZE9wJuKXWNOlOQG7TwMdzQrwIGjdrCUBQgPr/86ysLMJC7mBuboG9g9Mz9/XwwX0UCoXezaT8XyiVSj54dB9/7Sn38cceJw62dvZs2bkPnwoVSzDa51Pop7Cvv/6a9PR0atWqxb59+3Byyv/lqFy5Mm3btuWDDz6gffv2XLlyhenTp7N69ern/qxLly4BYFbAGMFNmuT+kW7atIlevXo99/5L0qWLF/Hy9sbGJn+mHhsby+SJXwLQsVNnXYSmF+T8PKlx+H3NKnq+9jqLlq0s8IIzaMgwBg0Zprbc7+gR9u/ZRdPmLZ5ZRfqyyc7KIicnG9N/jNuuVCpZOGsS4cGB9H3zXRxd9K+a+LHHNQ5darhw8NZDpu+6pTFxgNxhV5PSs6lR3poGnrac+cfsyg4WJvSqk3ucF/8x9OqVyER8HC0Y3tiDb/cG5O1bAYxomjsD84XwJ/vRJ49rHFpVdOBkSBy/+GlOHACOBMVyJEh9CNGqLpbUdbPhxoNkfjuVv6nT7YcpeNiVoXetsiz2D8vbtwJ4vU5uc6VrL+lQra0792Dh91PZt+0v+g59hwqVc4fdzMrM5Lf53wLQtnMvHUZYOMlJifmaJAHs37mZrRvWUL3WK7Tt3CNv+euDR3DhzAmW/DSTn5ZtwOTRS8vgoNts+3sdFpZWNG3VrkTjL04eXj40b90Ov8MH+HPtCvq9MSxv3ZL5c0hMiKfH6wMwMjIiLjaG6KgH+P5rOFaVSsX8778h+mEUTVq0zjtnpd3jGgdt7uPwj8TB1o4tO/dRoaJ+jypZ6OThwIEDKBQKli5dqpY4/JOjoyO//vorjRo1Yv/+/YX6rBEjRmhcfvToUQICAujRowdOTk54eXkVav8lac3qFaz4bRktW7XBw9MDC3MLwsLC2L1rB8nJyfR6rQ/9Bw7SdZg6I+cHZs+czu9rVmFhaUmFir7M+U59xs0u3XtQ8wWuTdi+YTVXzp0EIPj2DQB2/LWGi48mb2vevgst2ncFIPTObdYt+QmAzIzcZhFhwQHM/HwMADZ2Drz32ZPO5LExUQzv1oz6zdpQzs2TrKxMzvgdJOxOAI1bdeTtTyaWzEEW0rAmHnSp4UJqZjYRcWkMbeyhVuZYYAyBD1PIylGx4Ggwn3X0ZdZr1TkZHEtobBp25sa0qOiApakRGy/e5c4/RhpafSqcZhXs6VzdhUoulrnNl1RQ192GCk4W3EtIZ+2Z/96PrTj0rlWWVhUdSMvK4V5SBq/VVO9/cDY8gdC4wjUd2XzlAfXcbGhZwR5vhzK5iYIKqpW1xMOuDFHJGWy79vS5InRp24ZVXD53CoA7t68DuX9rFx79XbVo34WWj/+ugm6z5tdHf1ePmp2E3QlgxqO/K1s7e8Z89mSGdgtLa8ZP/5FJ44bxTv+OtO7UAytrG876HyE44CaNW3Xg1d6l79r95mvtcCnninfFypiamnL10nnOnTyGq4cX3/2S/4GwU/c+HNy9lQO7tjCgSzOatGxHclIiB3dvJTMjnalzFmNt82KNKDTl2x/p360tX30yhv27tuFTsRLXr17ipN8RXN08+GzSDADu3Y2gV/um1Kpbn4qVquDo7EJcbAxnT50gOPA2Ts4uTJ5ZMkP+l4TnuY/fvnWTIQP6kJGRQbMWrdi44Q+1su6engwa/GZJhK6VQicP8fHxWFpaUr9+/WeWbdCgAZaWlsTHxxfqs5YuXapx+bBhwwgICGDChAk0bty4UPsuaa/1fp2EhETOnDrJcb+jpKamYm9vT5NmzXnjjSH07T+gyCbUK43k/EB4WAgAKcnJzJ2tPkY2gIen5wudPFw5d5I9m9fnW3b1/Cmuns998Cnr6pGXPMQ+jFIrGxf9ZJlLefd8yYOlpTXN2r3K1fOn8T+8FyMjI7x9q/Lp1z/Spc8bT22Pqg/KWue+mTM3MdKYOADcS0gn8GHuXA87rj7gXkI6fV9xpVo5Kxp525OelUPQwxS2X3nAnhtR+baNSs5k5JqLDG7kTmMvO3rULIsKeJCYzh9nI1l9OpzEf0wqp08cH80AXcbYUGPiAPAwObPQyUNsahZf7LhFz5ou1ClvTTtfB1RAdHImO65HseXKA5Izc565H125fO4Uuzf9nm/ZlfOnuPLo76qcq3te8hAbHaVW9p/Lyrq650seAFq278q81dtYtXAOfgd3kZGWhpuXD+98OoX+w98rlU1SOnbrzcHd27h68SzZWVmUd/dkxPv/Y+iocWo1EgqFgm/m/cYfKxez5c/VbFy3HGNTU2q90pC33vuEeo3VZxwu7Ty8fNi4x4+fZn3NsUP7OH7kAI7OLrwxfDTvf/w5Dk65o1K5unkwetynnD5xjCMH9pAQH4epqRmePhV476PPePPtMdjZl56BKp4lTIv7uPuj+3jUgwd5I4Ju/Es9cQBo1rylXiUPCpWqoErdp/Px8SEqKorkZO2qaC0tLXF2dubOnaKbfnzYsGGsXLkSf3//50oeEhMTsbGx4X50PNbW6jOFClGQVD1+MNC1qxG6HwJZn03ccV3XIegtNyf97oCtS+81enHayBc1c5PSl4yUFKsyL37fwP/C4dGLBvFEYmIi3uUdSEhIeOazcaFfsXXp0oW0tDQOHjz4zLIHDhwgNTWVbt26FfbjhBBCCCGEEDpW6ORh4sSJODs7M2LECG7fvl1guYCAAN5++23KlSvHV199VdiP02jFihWoVKpS02RJCCGEEEKI0kyreq2jR49qXD5z5kw++ugjateuTd++ffOGalUoFERERHDo0CE2bNiAmZkZc+fO5ebNmzg7OxfpAQghhBBCCCFKhlbJQ+vWrZ/ZSXXt2rWsXbtW47qMjAxGjBiBQqEgO1s/O9oJIYQQQgghnk7rHjWF7Fdd5PsQQgghhBBC6IZWyYNSqXx2ISGEEEIIIcQLTb8HNBdCCCGEEELoDUkehBBCCCGEEFqR5EEIIYQQQgihlSKZgjAiIoITJ04QERFBSkrKUztGT5o0qSg+UgghhBBCCFHC/lPyEB0dzTvvvMPmzZufOZKSSqVCoVBI8iCEEEIIIUQpVejkISUlhdatW3Pjxg1MTEyoXbs2p0+fxsTEhIYNG3L//n0CAwMBsLe3p2bNmkUWtBBCCCGEEKLkFbrPwy+//ML169epXLkyd+7c4eTJk0BuonD06FFu375NcHAw/fr1Iz4+ns6dO3Po0KEiC1wIIYQQQghRsgqdPGzatAmFQsHMmTMpV66cxjKenp6sX7+efv368cUXX3DgwIFCByqEEEIIIYTQrUInDzdv3gSgc+fO+ZZnZWWplZ0xYwYqlYr58+cX9uOEEEIIIYQQOlbo5CE9PR1bW1tMTU3zlpmZmZGcnKxW1tvbGxsbG06fPl3YjxNCCCGEEELoWKGTBxcXFxITE1EqlXnLnJycyMzMJCIiIl/ZnJwcUlJSiImJKXykQgghhBBCCJ0qdPLg6emJUqnk7t27ecvq1KkD5PaH+KetW7eSnZ2Ns7NzYT9OCCGEEEIIoWOFTh7atWsHwMGDB/OW9e/fH5VKxYQJE5g9ezb79u3j+++/Z/jw4SgUCl599dX/HrEQQgghhBBCJwqdPHTv3h2VSsXatWvzlg0cOJDWrVuTmprK559/TufOnfnss89ITEzExcWFKVOmFEXMQgghhBBCCB0odPJQt25dlEole/bsyVumUCjYsWMHEyZMwNvbGyMjIxwcHBg8eDAnT56kfPnyRRK0EEIIIYQQouQVeobpgpQpU4YZM2YwY8aMot61EEIIIYQQQocKXfMghBBCCCGEeLlI8iCEEEIIIYTQilbNlsLCworsAz08PIpsX0IIIYQQQoiSo1Xy4O3tXSQfplAoyM7OLpJ9CSGEEEIIIUqWVsmDSqUqkg8rqv0IIYQQQgghSp5WyUNwcHBxxyGEEEIIIYTQc1olD56ensUdhxBCCCGEEELPFfk8D6WJQqFAoVDoOgy9o1RK87ICyakpUFBCkq5D0GvZ2Updh6C3ypgY6joEvWViJIMiFsTJ2lTXIegtW3NjXYeg14zl70pNtqn2KYGcPSGEEEIIIYRWJHkQQgghhBBCaEWSByGEEEIIIYRWJHkQQgghhBBCaEWSByGEEEIIIYRWJHkQQgghhBBCaEWSByGEEEIIIYRWJHkQQgghhBBCaKXIkgeVSkV0dDRhYWFFtUshhBBCCCGEHvnPycP58+fp3bs3NjY2uLi44OPjk299XFwco0eP5p133iEzM/O/fpwQQgghhBBCR/5T8rB69WqaNGnC5s2bSU5ORqVSoVKp8pWxs7MjODiYX3/9lX379v2nYIUQQgghhBC6U+jk4caNG7z99ttkZWUxbtw4zp49i6Ojo8ayQ4cORaVSsWXLlkIHKoQQQgghhNAto8JuOHfuXDIzMxkzZgw//vgjAIaGhhrLtm3bFgB/f//CfpwQQgghhBBCxwpd83Dw4EEUCgWfffbZM8uWL18ec3Nz6UwthBBCCCFEKVbo5OHu3btYWFjg5uamVfkyZcqQlpZW2I8TQgghhBBC6FihkwdTU1MyMzPVOkhrkpaWRnx8PDY2NoX9OCGEEEIIIYSOFTp58PLyIisri4CAgGeW3blzJzk5OVSrVq2wHyeEEEIIIYTQsUInD507d0alUvHTTz89tVxMTAzjx49HoVDQtWvXwn6cEEIIIYQQQscKnTx89NFHWFpasmjRIqZOnUpSUlK+9Wlpaaxbt4769esTHByMg4MD77zzzn8OWAghhBBCCKEbhU4eXFxcWLduHcbGxkybNg0nJydiYmIAqF69Ovb29gwZMoTQ0FBMTU35/fffsba2LrLAhRBCCCGEECXrP80w3a1bN44ePUq9evXIzMwkOzsblUrFjRs3yMjIQKVSUbduXY4ePUq7du2KKmYhhBBCCCGEDhR6krjHGjZsyOnTp7l8+TJ+fn7cvXuXnJwcypYtS7Nmzahfv35RxCmEEEIIIYTQsf+cPDxWq1YtatWqVVS7E0IIIYQQQuiZ/9RsSQghhBBCCPHykORBCCGEEEIIoZVCN1tq27btc2+jUCg4cOBAYT9SCCGEEEIIoUOFTh4OHz6sVTmFQgGASqXK+7eAs2fOMH3aZE6d9CczM5Oq1arz/rgPGTBwkK5D07mqlbwJCw3VuG7EyFHM+2VRCUdUsu7djWTLpr/Yv3c3AbdvEfXgPnZ29jRs3JSxH35CvQaNNG4XGhLMD99/y+GD+4l6cB8bG1sqVanKW2+/Q8/XXi/ho/hv/HdtIuDiaUJuXiUy6BbZWZkMnzib5t36FrjNw7vh7FjxC9dOHSMx5iHmltaU865Im9eH0KBd/gkqM9PTObRxDSd3byL6bjgqlQqHsq40aN+Ntn2HYm6pv8NKO1qa0LayE0197PFwKIODhQmJadlcjkxg7ekIrt9LUtumWjkr3mzsTk1XG8xNDLmfmM7+mw9ZdTKczGxlvrJlrU3Z+I7m7xjApK032H/zYZEfV1GwLWNEfXcbapWzoqyVKTZmRqRk5hAYncqumw8Jjk3LV76ljx21y1vjamOGtZkROUoVMamZXIhMZP/tGFIyczR+jqGBgnYVHWjkaUNZK1MAYlKzuBWVwtrzd4v9OAtr1+Y/uHjGn5tXLxJ0+zpZmZlM/O4Xur3+hsbyyUmJLJ33LYd2byUmOgoHR2fadO7ByHGfY2n17L+Rj0b048ThvZiYmHLsxoOiPhydWDhvDjOnfgnA5t1HeKWA63FYaDA//zCLY4f28zDqAdY2tvhWrsKQt0bTrWefkgy52Kz/fQ3+x/24eOE8165eITMzk4VLlvHGkGH5ymVlZbFz+1Z27dzO2TOniQgPw8DAgMpVqjFo8FDeGjkKQ0ND3RyEjpT2Z8BCJw+TJ09+6vqEhAROnTqFv78/Dg4OvPvuuy/dl6MgR48cpnuXTpiYmNC33wCsbWzYsnkjw4e+QVhoCOM//0LXIeqcjY0NY8Z+oLb8lVde/NG7fl30C/N+mI23TwVat2mHo5Mzd4IC2bl9Czu3b2HJ8jX06p3/Ifrwwf0MHZh7Q+r4aje8vLyJj4/j+tUrHDl0oNQlD5sWf0/MvUgsbe2xcXQi5l7kU8tfO3WMn8ePAqB283Y4uXqQmpRAROBNbpz2y5c8ZGdnMfu9Ady5dhH3StVo2qUPCoWCm+f82bToe07t3cpXy7dgalamWI+xsPq+Up4hjT2IiEvjTEg8camZuNuVoYWvIy19HZm87QYHb0XnlW/l68DXPaqSo4LDt6OJTcmkpqs1bzX1pJ6HLeP+uExWjkrtc24/SOZYYIza8jvRKcV6fP9FO18HulR15kFSBtcfJJOYno2LlSl1Xa2p62rN4pPhnA1PyCvfxNMOcxNDAqJTSEjLxshQgY+DOT2qu9DMy47p+4NITM/O9xnmxgZ81MobHwdzAqJTOBIUC4CjhQkNPGz0OnlYPHc69yLDsbV3wNHJhXuR4QWWTUtN4d1BXbl9/QoNm7ehY/fXCbhxld9/W8C5k8dY8sduyphbFLj91g2rOXl0P6amZqhU6t+v0ijg1k3mfjsNcwsLUlMK/js4emg/bw/tB0D7Tl3x8PQiIT6eG9ev4Hfk4AuTPEyfMomwsFAcHB0pW7YcYWGaX/oF3wliyKB+WFlZ0bJVG7p07U5iYgK7dmznkw/fZ/++3azfsPmlecH8IjwDKlTF/Fd98OBBevfuTfv27fnrr78KvR8vLy9CC3gbPXr0aBYt0v5tdGJiIjY2NjyISSjxieuys7OpXaMKkRERHD7mT526dQFISkqidYsm3L51iwuXr1PR17dE4/onpVK3F/qqlbwBuHE7WKdxaJJWwJvIorR9yyYcHJ1o0qx5vuX+x/3o3b0jlpZWXA0Iw9Q0941nZEQ4zRvVwcnJhY3bduPm7pFvu+zsbIyMimxgtQJtuf70B/zncf20H87uXjiWc2PnygX8vWBWgTUPsQ/uMmlgJ6ztHfnk5zU4lHXNtz4nOxvDfxz/mf3bWfTl+7zSujNjvst/3fh5/CguHNnLiMlzaNqlaG/wSw9rvn49r1a+DsSnZXEpIjHf8tpu1szrX4vUzBx6LDhJVo4KEyMDNr3TEEtTI0atucitB8l55T9uX4HXX3Fl4ZFgVp968hD5uOZhx5X7zNh1u0hifpaqnnZFsp9XXK1JysgmIDo133JfR3M+be1NeraST7beJPvRNc7IQJH373/qVcOF7tWd2X3zIRsu3c+37r1mHtR1tWbpyXBOhSXkW2eggKK+fI5u4PHsQlo6ffww7l4+lHP1YOWiH1gwe2qBNQ9LfvyGZfNnMWTUB7z/2VS15SPGjmfUh5ofch7ci2TQq03o0W9Ibq3Fw6hiqXkoZ2tW5PssSE5ODr06tUKhUOBdoSKbNvyusebhbmQ4HZrVw9HZmXUbd+Lqppvrsa25cbF/xqGD+6lQwRcPT0/mzv6OKZO+0FjzcDcykp07tjJo8JuYm5vnLU9JSaFLx7ZcOH+WlWvW81qfgmuWi5qxkW66/OrzM2BiYiIuDjYkJDz72bjYz17btm356aef2LRpE0uXLv1P+7KxsWHy5MlqP926dSuiaIvf4UMHuRMURP8Bg/K+NABWVlZ8/sVEsrOzWbVyuQ4jFLrWredraokDQJNmzWnesjVxcbFcv3Ylb/kP339LUmIi3//0i1riAJTIjaqoVWvYHMdyblqV3bHiF9JSkhjy+Qy1xAHIlzgAPIwMA6Bm09ZqZWs2bQNAYqz6G3d9cSQgRi1xALgUkcj5sHhsyhhTwSn3jXAtV2vszE04GhCTL3EAWHIsBIBedcoVe8wl5XxkolriABAQncrNqBQsTY1wtXnywKkpcQDyaiecLU3yLfe2L0M9NxtOhsarJQ5Q9IlDUWvYrDXlXJ+djKhUKrb+uRpzC0tGjB2fb92b736MtY0tWzesKbBGYcaEsdjaOzL646+KJG59sPCn77lx7TKz5y9+aiuKn+fOIikpkW++n6+WOEDpvB4XpE3b9nh4ej6zXHlXV0aOejdf4gBgYWHB++M+BMDv2NHiCFHvvCjPgCXyLe7fvz9vv/02S5cuZeTIkYXej62tLVOmTCm6wHTg6JHDALTr0FFtXftHy44dPVKSIemljIwM1qxeyb27kdja2tGoSVNq1aqt67B0zsjI+NF/c/90VSoVWzb+hb29Ay1bteHihXOc8DuKUqmiZq3atGjVBgODF3dQNZVKxZn9O7C0saNq/aaE3LjCrQunUCmVeFSqRpX6TdWOv7xPJQCu+h+hZc8B+dZd9T+MQqGgSr3GJXYMRenxw3DOo//aW+Q+/N5LSFcrm5yRQ2JaFuVszHC1NSMyPn8ZJ0tTXqtTDktTI6KTMzkbGsfD5MxiPoLik/PoQVepRWV7rfJWAEQmZORb3tDDFshNLixNDKnjao21mRGxqVlcuZdUYB+J0iY8JIiHD+7RuEU7taZJpqZm1GnQlKP7dxIecgcP7wr51m/6fQWn/Q6xYO02zPS06d/zunXjGj/OnsHYTz6ncpVqBZZTqVRs3/I3dvYONGvZhssXz3PqxDGUSiXVatSiWcsX+3pcGMbG+e9pL7oX5RmwRP5vmZmZYWFhwY0bN0ri4/RaYGAAABUrqldJ2dnZ4ejoSNCjMi+zB/fvM3rk8HzLOnTszNLlq3B0dNRRVLoVER7G0cMHcHYpS7XqNYHcTtJxcbHUrVefTz8cw4plS/JtU7N2Hdb+sYnyrtq9xS9tou+Gk5IYj3e12qz+7ksOb1ybb71H5eqMnb0Ue5cnb9drN29H7RbtOXdoF9OGdqPSK7nNDm6dP0lUeCiDx3+NV9XSN+Gli5Up9T3tiE7OIOhhbnvs+NQsAMrZqDfvsDAxxLpM7o3b3a6MWvLQ0NuOht5PmhNl5yjZcP4uPx+6g56/YFdjb25MNRdL4tOyiNCQSDXzssXBwgQzYwM8bctQxcWS0Lg09t7K3zHcyy73YdjZ0oSRjdwxN3nyBjo9K4cVZyI5E65eI1HahIUEAeDu5aNxvbtXbsIQHhKUL3m4FxnGvG8n0nvQW7zSSL32tDTKzs7m4zFvU7FSFd774H9PLRsWGkx8XCy169bni0/GsmbFr/nW16hVh2Vr/qLcC3o9LozVq3Lfsrdt30HHkZSMF+UZsESSh8jISBISErC0tPxP+8nIyGDlypVERkZiZ2dH06ZNqV27dL2NTkzIvbHY2NhoXG9lbU1kRERJhqR3hr45nOYtWlG1WnVMTU25eeM630yfxt49u+jXpycHDvu9NB2rHsvKyuLdt4eRkZHBlK9n5lWbRz+MAuDyxQvcvnWT+QuX8mrXHiQmJvDD99+yesUyhg/uz55Dx3UZfrFJjM3tGBx66yp3QwIZPnE2dVt2JC05kR0rFnB0y+8snPAuX/62OW8bhULB+98t5q9fvmPvul8JvXU1b12TV3tTo3Grkj6M/8zQQMGkbpUxNTLgu8PBec1nrkQmkJyRTUtfByo5W3A76kknz1EtvPL+bWX25FaQka1k2fFQjtyO5m5COiaGBlQvb8V7rbwZ2MCNrBwli46GlNCR/XeGChjZyA1jQwP+uhSJpoqHpt52VHF+cn+6ei+JpafCSc3KPxKVlVnu313f2uU4GRrP1msPSMnMoVZ5awa/Up6Rjdy4l5ihMUEpTVKScpvFWVppvk9ZWObWzCQnPWk+p1KpmPH5WKytbRgzfkqxx1hSfv7hO25cu8yWvcfy3pIXJOZhbrJ59fIFAm/fZM78JXTo0p2kxAR+/mEWv6/6jXeGD2LL3pejic6zLF+2hH17dtOqdVs6de6i63BKxIvyDFjsyUNaWhrvvfceADVr1vxP+7p//z7Dhg3Lt6xz586sXr36qW+jMzIyyMh4Uv2cmKjeXljojwlfTsr3e4OGjfh78zY6tW/NieN+7Nm1k85duhaw9YtHqVQy7r2R+B8/xpBhI+g3cHC+dZDbmW/CV1MYOPhNAGzt7Phh/iKuX73CubOnOXnCj8ZNX4w3gf/0uM21MieH10Z9nNeh2sLahje/mElE4A3uXLtIwMUz+NZpAOQO07r4q/e5c+0ib0/7ieqNWgAKbpw9zu9zpnDF/zBfLtuEs9uz2/LqAwXw5auVqOtuy5ZL99h9PSpvXVqWknkH7/DFq5VYPLguh289JObRaEuVXawIiUnFy8E8r5kTQFxqFsuOP+ncnUoOx4NiuXE/iTXD6zOgvhtrT0WQlJF/FCJ9pACGN3SjsrMlR4Ji8Q+N11hu9qHcwRksTQzxcTDn9dplmdTRl5+OhuRLBB6/tIhISOe3009u8KdC4zEzMmBofVfaVXJg5ZmiGzygtPh77TLOnDjCj7/9lZdclHbXr15m/pxvGTXmQ2rWrvvM8krVk+vxJ19Mpu+goQDY2trx3Q8LuHntKhfOneb0yeM0bNysWGPXd7t37eDTj8bh4eHJr7+t0nU44jkVOnmYNm3aU9enp6cTHh7Onj17iImJQaFQMGbMmMJ+HG+99RatWrWievXct9HXr19n6tSp7Nq1ix49enD8+PEC30bPnDmTqVOnalxX0qwfZZsJCZqrtpMejQQl8jMwMGDw0GGcOO6Hv//xlyZ5UKlUfPT+aDasX0ffAYOY89OCfOut//Fd6dylu9r2nbp049zZ01y8cO6FTB7K/OMhpU5L9Wrv2i3ac+faRUJuXM5LHnauXMDFY/sZO/vXfNs0aNcVM3MLfvxwGNuW/cSIyXOL/wCKwOedK9G5ugu7rz1g1h716u7tV+4TnZzBG43cae7rgKFCwc37yYz74zKDG7nj5WBOfFrWMz8nNiUL/zuxvFrDharlrDgdElcch1Ok3mzgShMvO/xD4lh99tkP9MmZOVy+l0R4fDozu1bizQauzNgflLc+LSu3T8Oluxo6rN9NBFzzmjaVZhaP5nBITtJ8n0pJzp1L5PFcD1H37/LLrCl07TOIJq3al0yQJeDjMSPx9PLho88malXeyvrJ9bhDZ/V7VPtOXbhw7jSXL55/qZOH/fv2MGRgX5ydXdi2ez9ly704gzY8y4vyDFjo5GHKlClaNR1RqVQYGBjw5ZdfMmhQ4Se/mDQp/9voRo0asX37dlq1aoWfnx87d+6ka1fND5QTJkzg448/zvs9MTERd3f3QsfyXzxu5xYYGMAr9erlWxcXF0d0dDSNmzTVRWh6z9Eht3YpNVV9NJUXkVKp5MMxo1i3ZiW9+w7g50W/qXW28/apiKGhITk5OdjY2Krt4/FFKD2tdDejKIizmycGhoYoc3Ioo2Fit8eTvWVmPDn+y8cPAlC5XhO18lXqNUGhUBBy86raOn2jACZ0rkS3WmXZez2K6TtvFdgX4WRwHCeD1R/2fRwtyFGquHU/WcNW6hIeJRlmOhrmUFsKYFgDV5r72HMyNJ5lpyOeq59GXFoW9xIz8LIvg4mhgsxH82DcT8zA296ctEyl2japjzpLmxjq97nRhkden4Y7GteH5/WJeNL3ITUlmR1/r2PH3+s0btOogi0A+y+EYGVtW7QBF5PrVy8D4Fte88Ncr865TRx/XfUnnbr2wMu7wlOvx9aPlqWnpamte1ns27ubN/r3wcHBke17DuDtrblfzYvqRXkGLHTy0LJly6cmD0ZGRtjZ2VG7dm369euHbzGMWWtgYMDw4cPx8/Pj+PHjBSYPpqameWPi61qLlq2Y/d1MDuzbS7/++Ud62b9vb14Zoe7MmVMAeHp66TaQEvDPxKFXn34s/HWFxuEBTU1NadCoCSdP+HHr5nW12oVbN3MHKXDXYji90sjYxJQKNesRcPE094ID8moXHrsbnPsm/p/DvmZn5z4AJ8fHUsYifz+s5Pg4VCoVxsb5h+jUN/9MHPbfiGLajpvPPUxoTVdrytuacSKo4JmU/61qudyannuJ+puM/jNxOB0Wz9JT4Rr7OTyLjZkRqPIPv3ozKoUmXnaUs1G/n5R/1Ck9OqX0jkj1mLtXBZxcynH5/CnSUlPyjbiUkZHOxTMncHIpl9eh2tG5LD36DdG4r/07NpGRnkbXPrkvD41N9ONerI0Bg4dpXH7K34/goEA6dO6Gg6Mjbh6511dTU1PqNWzMaf/j3L51Q612IeDWo+uxx4t5PX6WfXt3M6hfb+zs7Nm+5wAVKlTUdUgl7kV5Bix08nD48OEiDKPwHvd1KC1vo9u0bYe3jw9/rF/He++Po3adOkDuBCHffvM1RkZGDBk6TKcx6tKNG9cpV648tra2+ZafOO7H/J9+wNTUlJ69eusmuBKiVCr54L23+X3tKnq+9jqLlq586rjiw0eO5uQJP2bN/Jrf/9qalygH3LrJ+rWrsLSyol37TiUVfolr0/sNAi6eZsvSH/lg7m95Dyf3QgI5vuMvzMwtqdGkdV75irXqERl0i61Lf2T4V7MxeHRulUolm5fkNlXSVCuhLxTAF69WomvNshy4+ZCp25+eOJibGOa9FX/M0dKECZ0rkZ2jZIlf/snrqpa14nZUcr5+EAAD6rtS282GO9EpBETp5yzTCmBYQ1eae9tzJiyeX08WnDhYmBhiY2bE3cQMtXU9qjtjU8aY6/eT880FcTY8gddrlaWxhy37b0fnDeVqaKCgZ3UXgBditCWFQkGPfkNYNn8Wy+bPyjdJ3MqFc0lMiGfE0FF5LxA9fXz5cuZ8jfs6c/wwMQ+zClyvz2b9pHny2Y/HjCQ4KJAxH/5PbZK4IcNHcdr/OD98N50V6zfnXY8Db99iw/rVWFpa0aqd+jCdL7rHiYOtnR079hzQONrQy+BFeQYs9QPrnjqV+zbay8tLt4FoycjIiIWLl9K9Syfat2lBv/4DsbK2ZsvmjYQEBzNl2nR8K1XSdZg6s/GvP/lhzmxat2mHp6cnJqamXL92jQP792JgYMC8nxfi7lF0M67qo9nfTuf3tauwsLSkQkVf5sz6Rq1Ml249qFmrDgC9X+/Pjq2b2br5b1o1qUfb9h1ITEhg29ZNpKen88uS5djaFc0MviXl6Jb1BFw6A0Bk4C0Ajm35g1vnTwJQt1VHXmmVmxA17NiDc4f3cO7gTqYMfpXqjVqSlpLEuUO7ycrMYOTkuVj8oy1yt+Hvc/HYfk7s3EjIzatUrZ9bRXzr/EkiAm/iWM6NLkPfKcnDfS5vNfOka82ypGRmEx6bxrCm6m8xjwZE5z3g96vnSqdqzlyKTCAuJQsXa1NaVHTA1NiQmbtuc/tfk8eNae2Np705FyLiiUrMwNTIkBquVlR2sSIxLYtpO26VyHEWRvfqzjT3tic9K4cHSZl0q+asVuZCZCLh8enYmxszpZMvd2JSuZuYTkJaNlamRvg6mVPO2oz4tCzWnM/fTyI9W8nKs5G829SDL9tX5Gx4AqlZOVRzscTVxozLdxM5rsd9Qbb8sYpLZ/0BCLx1PXfZn6s5f8oPgFYdutKqY+6kq0NGfcCxA7tYveQnbl2/TJUadQi4cRX/I/uoVK0mQ0Z9oJuD0HM9evdj1/Yt7Ny6kc4tG9CybQeSEhPYtW0zGenp/LBgGba2pet6XJCVy5fifyJ3JL/r164+WvZb3jwF3br3pFuPXty+dZNB/XqTkZFB8xat2PDnerV9eXp6qs1M/SJ6UZ4BC508GBgYYGBgwM2bN6lYsXirnq5fv0758upvo/38/Jg7dy6mpqb07l163ka3at2GA4f9mD5tMn//9SeZmZlUrVadSVO+ZuCgN3Qdnk61bNWGWzdvcvHCefyOHSE9PR1nFxf69O3P2HEfUr9BQ12HWOzCQ0MASElOZu7smRrLeHh45iUPCoWCJcvX0KBRY9auWsHK337FxNSUho2a8OGnn9OsecsSirzoBFw6w4kdf+dbFnj5LIGXzwK5zZAeJw8KhYLRX8/jQK1XOLb1T45sXoexsSkVa75C12FjqPxK/gnf7F3KM3nVdnasWMCVE4c5smkdKMChrBsdB42k67AxWNro7829rHXum0wLEyOGNdWcSN9LSM9LHq5EJlLH3YbmFRywMjMiIS234/OaU+H5hm59bM/1KFpXcqRmeRtsK+TeIu4nZvDH2QjWnY7Q64niHB9NimdmbEi36uqJA0BMSibh8enEpGSy43oUlZ0tqFnOCgsTI7JzlDxIzmTbtSj23Y7W2JzrQmQisw7doVs1Z+q4WmNiqCAqOZMNl+6x91Z0oZpIlZRLZ/3ZsfH3fMsunzvJ5XO5SXk5N4+85KGMuQUL121n6bzvOLhrK+dP+eHg6MLAt95j5LjP1CaPE7kUCgU//7qKFQ0bs37NCtatXIqJSW5zpvc/Gk/jZqXvelwQ/xPHWbcm/0hJJ/2Pc9I/N6Hw8PSiW49ePHhwP2/Ey783/KFxX81btHopkgd4MZ4BFaqC5pd/BgsLC4yNjYmPjy/ikNRNmTKFWbNm0a5dO7y8vDA1NeXq1avs3Zv7NnrRokXPNXN14qPe7A9iErC2Vu9k+bJTPm/j6ZdI2gsyg2xx2HL95Rue8nksPRz67EIvqaqe+pus6droBi92Tet/Uc5WffJDkcvW/OlzUrzsjPV80AddSExMxMXBhoSEZz8bF7rmwc3NjYgSmsiiTZs23Lhxg/Pnz3PkSO7baBcXF/r3789HH31Ew4Yv/ttoIYQQQgghdK3QyUPXrl356aefOHLkCK1aFW/P8FatWhX7ZwghhBBCCCGertD1NhMmTMDJyYl3332Xe/fuFWVMQgghhBBCCD1U6JqHGzduMGPGDD766COqVavGkCFDaNasGc7Ozk8dVrJlyxens5AQQgghhBAvE62Th1WrVlGmTBn69u0LQOvWrfNNEvfLL7/wyy+/PHUfCoWC7OzsQoYqhBBCCCGE0CWtk4dhw4ZRrly5vOQB4HkHairkwE5CCCGEEEIIPfBczZb++fCvVCqLPBghhBBCCCGE/pKBboUQQgghhBBakeRBCCGEEEIIoRVJHoQQQgghhBBakeRBCCGEEEIIoZXn6jD94MGDp87h8CwyVKsQQgghhBCl13NPEifDrQohhBBCCPFyeq7kwcLCgk8++aS4YhFCCCGEEELosedKHiwtLZk8eXJxxSKEEEIIIYTQY9JhWgghhBBCCKEVSR6EEEIIIYQQWpHkQQghhBBCCKEVSR6EEEIIIYQQWpHkQQghhBBCCKEVrUdbUiqVxRmHEEIIIYQQQs9JzYMQQgghhBBCK5I8CCGEEEIIIbQiyYMQQgghhBBCK5I8CCGEEEIIIbQiyYMQQgghhBBCK5I8CCGEEEIIIbQiyYMQQgghhBBCK5I8CCGEEEIIIbSi9SRx4uWhVKl0HYL+Uug6AP3VoLyDrkPQa+Yd5HJbkMVHQ3Udgt6ae+yOrkPQW+Oaeuk6BL1lYmSp6xD0moXcy9Vk52g/GbTUPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQitGug7gZXX2zBmmT5vMqZP+ZGZmUrVadd4f9yEDBg7SdWg6pVQq+XXxQlavXM7tWzcxMjKiVu26jP3wI7p266Hr8ErEvbuRbNn0F/v37Cbg9i2iHtzHzs6eho2bMvajT6jXoNFTtw8NCaZlo7qkpKTw5ltvM2feghKKvGSoVCoO7N7K78sXExx0m+TERFzKu9KgSQveevcj3Dy988r+tW45h/ftJPDWDWKjH2JkZER5Nw/adOzK4BHvYWNnr8MjKZxjO/7m1oXT3LlxhfDAm2RnZfLOlLm06tFPrexfi+bw95IfNO7H2MSUVSeDnvl5340bykW/g1qX1yVHCxNa+TrQyMsOD/sy2Jsbk5SezdV7Saw/G8mNB8lq27jamvFGAzdqlrfCydKExPRsQmPT2HTxHieC4/KVreBoTsuKDtT3sKWcjRkWJoZEp2RyJjSeNacjiE7JLKlDfW525sY09rSlrps15a3NsC1jRHJmDreiUth29QGB0an5yr9euyyv1ymncV+ZOUqGrrmUb5mJoYIOlZ3wcSiDl4M55axNMVAoGPvXNR7q8Xl5bM+WP7l01p+b1y5x59Z1srIy+eLbn+naW/2efPv6FQ7u2sytaxe5fe0y8XEx1G3YjJ/XbNO474AbV/lz5UJuXbvEwwf3SE9NxdGlHFVq1GbQyHFUrVm3uA+vWCTExzPrm6lcPH+WsNAQEuLjsHdwpELFSgx/+x269ngNhUKRV372zGnM+Xa6xn2ZmpoSGpVUUqHrzJpVK3h31IinlmnVui3bd+8roYgKR5IHHTh65DDdu3TCxMSEvv0GYG1jw5bNGxk+9A3CQkMY//kXug5RJ1QqFUPf6M+WTRvx8anA0GFvkZGRwY7tWxnw+mt8/8M8Rr87RtdhFrtfF/3CvLmz8fapQOu27XB0cuZOYCA7t29h5/YtLFm+hl59+mrcVqVSMfadkSUcccma8/UXrPr1Z5ycy9KmYzcsray4df0qf69bwa4tf7Fq0358q1QDYPvf60lMiOeVhk1xcnYhMzOTy+dPs/in79j61zrWbj2Eo7OLjo/o+fy5YDbR9yKwsrXH1tGZ6HsRz9ymZfe+OJVzy7fMwOjZl/9Dm9dz6cRhjE1NQVXokEvMa3XKMqi+G5HxaZwLiycuNQs32zI0r2BPMx97pu++zeGAmLzyVV0smdunOkYGCk4Ex3E0MAa7Msa0qOjAjB5VWX4yjFWnnpzfj9tWoEpZS249SObQ7Wgyc5RULWtJz1plaVXRgXF/XSU8Lk0Xh/5Mnas40bOmC/cTM7hyL4mE9CzKWZlS38OWBu42zDsawsnQeLXtjgTG8DA5/8N/jkr9y2BjZsyQBq4APEzOICUjByuz0vOIseTHGdyPDMfWzgEHZxfuR4YXWPbY/h2sXvwDxsYmuHtXID4upsCyADeunMf/yH5q1GlAnQbNKFPGnLsRIfgd3MOh3VuZOGshnXqqJ//6LjYmmt/XrKBe/Ua82rUHtnZ2RD98yN7dOxg5dACD3xzB9/MWqm3Xb9AQ3D088y0z0uJ69CKoVbsOE76cpHHd5k1/c+P6Ndp16FjCUT0/hUql4SqgxzZt2sSCBQs4f/48qamplC1blsaNGzNr1izc3d212kdiYiI2NjY8iEnA2tq6mCPOLzs7m9o1qhAZEcHhY/7UqZv7xiEpKYnWLZpw+9YtLly+TkVf3xKNK1+MOUqdfO7mjX8xZFB/GjdpxtadeyhTpgwA0dHRtGrakAcP7nPu0nU8vbx0Eh9ARnbxn5vtWzbh4OhEk2bN8y33P+5H724dsbSy4mpAGKampmrbLlkwn0lfjGfy9JlM/Px/JVrzcDcuvdg/IzrqAe0bVKKsqzt/7TmBpdWTv9/VS39h9tTP6dVvMNPm5N6wMtLTMTUzU9vPz7O/Zsm8Wbw5ehyffDWj2OMGuPYwoUj2c+XUMcq6e+NU3o0ty39m/fxvn1nzMHHJn1Sr3/S5PifmwV3G921P614DOH1gJwkxD4ut5mHx0dAi2U+LCvbEp2Vx5W7+N5g1y1sxp3d1UjNz6LvsLFk5ube9mT2r0tjLji+33shXy+BsZcKyN+pgZKCgx+LTeeV71SrL6dA47iZk5Nv/gHqujG7uycngOCZsvVEkx/KYi22ZItlPAw8bktKzuRmVkm95FWcLvupYkbQsJe9uuEq2MvdYH9c8TNsdwHUNNTb/ZmpkQCUnC+7EpJKSmcPn7StQx9W6WGsexjX1KrJ9nTl+GHevCpR1dWf14h9ZNGdagTUPdwJukJWZSYVK1UiIj6VHs6pPrXnIyEjH1FT9OnQn4AYjerfDwtKKbSdu5ntL/19VcLEssn0VJCcnB5VKpfbgn5yURJf2zbl98waHT16gStXqwJOah7+376NZi1bFHt/TWJga6vTz/y0zMxNfbzcSExK4FRSGs0vJv9RKTEzE1dmOhIRnPxuXmj4PKpWK0aNH07t3b4KDgxkwYAAffPABLVq04MSJE4SGFs3Np7gdPnSQO0FB9B8wKC9xALCysuLzLyaSnZ3NqpXLdRih7mzfugWATz/7PC9xAHB0dGTMuA/JyMhgzaoVOoqu5HTr+Zpa4gDQpFlzmrdsTVxsLNevXVFbfycokOlTvmLsh59Ss1bprAZ/lsiIUJRKJXXrN86XOAC0bNcJgNiYh3nLNCUOAB27vQZAeMidYoq0+NRs1AKn8m7PLvgfLZn6P6zt7On/3v+K/bOKyrGgWLXEAeDK3SQuRiRgU8YYbwfzvOXlrU1RqlSc/tcb96ikTEJiUjEzNqSM8ZOHjM2X76slDgB/no8kLSuH2q4l+zLqeZwJS1BLHABuRqVw7X4yVmZGeNhp/nvRRka2kiv3kkjJzPkvYepMg2atKeuq3QtIH9+qVK5eGyNjY63Ka0ocHu/Hq0Il4mIekpJc+prsGBoaaqwxsLSyok273LfnIXf0u6mjvti2ZROxMTF07tJVJ4nD8yo19UTz589nyZIljBkzhp9++glDw/xZY3Z2to4iez5HjxwG0Fgt1f7RsmNHj5RkSHojKuoBAJ5e3mrrHtc2HDl8iC8nTSnBqPTL45vVvy/YSqWSce+MxM3dk08nfMWZUyd1EV6x8/SugLGJCRfOniQlOQkLS6u8dccO7gWgYdNnv9E6emA3ABUrVyueQPXMzQunCbx6EQNDQ8p7VaRmo+YYm6jXXD124O81XDl1lK+W/ImJWdG8+da1x2/Uc5RPKttDYtPwsDenvqctJ/9R8+BkaYKXgzlB0Skkpj/73qJSgVKpKg0tuzTKyTs36uuquFhQwdEcpQruJqRz5V5S3rkU/01EWDBhwYG4lHNVexlSmqWnp+N39BAKhYJKVaqqrT/l78eFc2cwNDSkYqXKtGzdTmNN+stk1YrfAHhz+NP7Q+iLUpE8pKWlMXXqVHx8fPjxxx/VEgcoPe3lAgMDAKhYUb1Zkp2dHY6OjgQ9KvOycXR0AnI7/Fb51wUnNCQEgMDA2yUdlt6ICA/j6KEDOLuUpVr1mvnWLfrlJ06f8mfHvsMv9EXY1s6Bsf+bxNwZX9GzTX1ad+iChaUlATevc9LvEK+/MZyBw99R227Ln2uIjAgjNTmJG1cvccb/GFVq1Gbo2+/r4ChK3oaF3+f73dbRhXen/UCtxi3Vyj68G8HaH6fT7vUhVKvXpKRCLFbOVibUc7clJiWT4JgnHYN/8w+jRnkrpnWtzPE7sUTGp2NbxpgWFe15kJTB1J3aXW9a+TpgYWrE4dvRxXUIxcbBwpga5a2IS80iLF69v0a/uuXz/R6bmsVCv1Cu3Ct9b8p17fb1Kxzbv4Ps7Gzu3w3H78AuAD6dOlfHkf03CfHxLFk4D6VSSczDhxzYt5vIiHA++fwrfCqoP+vMmjE13+8uZcsxb+EyWrVtX1Ih65Ww0FAOHzpIeVdXOnTsrOtwtFIqnrj37dtHbGwsw4YNIycnh61bt3L79m1sbW1p3749FStW1HWIWktMyG33bGNjo3G9lbU1kRHP7gD5IurQqTMb/lzP3NmzaNW6LWaPmpzExMSwYP5PQO5F6mWUlZXFuyOHkZGRwZTpM/Ml0IEBt5k5bTKj3htLg0YvxsPe0wx75wOcXMrx9YQP+HP10rzldeo3outr/THW0JRgy4a1nD3pl/d705btmPHTr1jb2pVIzLriWbk67077kaqvNMbGwZHYB/c4sXcrW36bz/cfDefrldvwrPSk9kWlUrFk2qdYWNkwaNyLMXCDoYGCLzr6YmJkwOIDofzzpXlobBpj/rjClC6Vae3rmLc8MT2L3dejiNTwMP1vTpYmjG3lTXpWDr/5hxXHIRQbQwWMae6JiaEBv54L4589IENi0/jlWCg3HiSRkJaNvYUxTb3t6FWzLP9r68NXO28Tpqedw/VVwM0r/PbzrLzf7R2d+WrWAho1b6vDqP67hIT4fKMoGRsbM+nrb3l37Ef5ytWoWZt5i5bRpFlLnJxduBcZweaNfzJvzne8ObA3O/Yfo3rN2iUdvs6tWbUCpVLJ4CHDNL4c10elos/D2bNngdzahdq1a9OnTx8mTJjAu+++S+XKlfn00091HKEoCn37D6RlqzacOH6MxvVq8+lH4/jg/Xdp+EpNrB513iktf1hFSalUMu7dkfgfP8aQYSPoN3BwvnVj3xlB2XLl+WLSNB1GWXKW/DSLiZ+8w4gxH7P39E1O3rrPyo17yc7OYWT/ruzftUVtm9827OJyeBJHLgXz84oNPLgfSf9Xm3P7xlUdHEHJadCmMy27vY5TeTdMTM0o6+FN75EfMPTTqWRlZLBp6U/5yu/bsIqrp/0Y8eW3lLEo/g6XxU0BjG9fgdpuNmy/cp99Nx/mW1/J2YJ5fWuQmJ7NqHWX6PzLSQYuP8fOa1GMaenN5C6Vn7p/K1Mjvu1ZFVtzY+YcCCI8vvgHDSgqCuCdZp5UK2vFgdvRHLuTf1jas+EJHLsTS3RKFllKFQ+SMtl0+QErT0dgYmRA71r63y5b33TtPYjjt2M5eOUuq7b50ahFOz4d2Y91y+brOrT/xMPTi/sJmUTGpnH68m3GfzGZb7+exIjB/fI1KX+1W0/6DcwdacnMzAzvChX56H9f8PW3c0lPT+eH2TN1eBS6oVQqWbNqBQqFgiFvDtd1OForFclDVFQUAHPmzMHa2prTp0+TlJTE0aNHqVSpEnPmzGHhQvXhwB7LyMggMTEx34+uWD+qcUhI0DzyStKjkaBeRkZGRmzcuoMvvpqMwsCA5ct+ZeuWTXTt1oPVv/8JPGna9LJQqVR89P5oNqxfR98Bg9RGTlqycD5nT5/ih58XY25uXsBeXhynjh/h5++/ZuCbo3h77P8oW84Vc3ML6jZows8r/sTUrAyzp04ocHs7e0datuvMwtWbiI+LYcr4sSUYvf5o1b0vhkZG3Lp4Nm9ZbNQ9fp/3Da2696NOszY6jK7ofNq+Ah2rOrP3RhRzD+bvHG9ooGDSq5VBBRO33yTgYQoZ2UruJ2aw2C+Ug7eiaVnRgTpumtuiW5oa8n3vang5mPPjwTvsv1W6miyNaupBiwr2HAuKZal/wcOS/tuRoFiylSoqOZf+5FJXTE3NqFC5Gl999wuNWrRj4eyp3Ll9Xddh/WeGhoZ4eHox9uPxfPbVVHZu38KaFcueuV2/QUMwMjLizCn/EohSvxw8sI/w8DBatW6Ll7d6f099VSqSB6UytxeXiYkJmzdvpkGDBlhaWtKiRQv++usvDAwMmDNnToHbz5w5Exsbm7wfbYd0LQ6P+zoEaujXEBcXR3R0NBU09Id4WZiamjLhq0lcuHKDmMQ0gsPvM++XRdyLjASgbr16Oo6w5CiVSj54723WrlpB774D+HnxbxgY5P+TvXr5EiqVil5d2uNoaZz306tLbtvRlb/9iqOlMUMG9NHFIRS5Y486Ojdoqt5W397BCd8q1bgXGU5c7NMf5MqWd8OnYmWuXTpHWlrqU8u+iIyMTTAztyAz/Umzk/thwaSnpnBk258MfMUt30/0vQiyMjPyfk9JKpphZ4vL4xqHLtVdOHDrId/tC1TrzOxhVwZXWzNuPEjWOATzhYjcY9T0kGxlasSc3tWp5GzJvMPBbLv6oBiOongogNFNPWjj68DxO7EsOB76XB29c5Qq0rNyMDUqumFFX2YNm7dBqVRy6eyLNchF60f9F074PXsAGBMTEywtrV7Ka/GTjtJv6TiS51Mq+jw8fhNfv359ypfP33mrevXq+Pj4EBgYSHx8PLa2tmrbT5gwgY8//jjv98TERJ0lEC1atmL2dzM5sG8v/foPyLdu/769eWVEfn+sXwfA63376ziSkqFUKvlwzCjWrV5Jrz79WLh0hcYmW02bt8RQw2ABD+7fZ/+eXfhWqkLDJk2oVatOCURd/LKysgCIi9GcHMTF5E7WZPKUkYQeexh1H4VCgaHBy9cU7l7YHVISE/L1d7B1dKFNrwEay/vv3UZmRjqtuudOTmhsbFIicRaGAvhf+wq8Wt2Fg7ei+WZPAJoGBzI2zH34tSmj+Tb4eHnWv4YgsjI14vve1ajkbMlPh+6w5fL9Io2/OCnIrXFo4+vAieA4fvYL5XlneiprZYqlqREhsS/fg15xiI7K/f68aE1y79+/B2g3mM2doADi4+OoXrNWcYelV2JiYtixbSt29vZ07/marsN5LqUieahcObfdqabE4J/L09LSNJYxNTXVmxFo2rRth7ePD3+sX8d774+jdp06QO4kcd9+8zVGRkYMGTpMpzHqUmJiotrkJJs3/sXqlcupV78BPXr11lFkJedxjcPva1bR87XXWbRsZYE3lkFDhjFoyDC15X5Hj7B/zy6aNm9RYpPElYQ69Rvz+4rFrPr1Z9p36YmV9ZMmfls2rCUsJIhqNetiYWlFfFwM0VFRVKycf+QulUrFwh9mEvMwikbNW2OiJ9eGopaWkkxUZFi+BAEgOTGeJVNz525o2rln3vLyXhUYNSn/qEyPXTnlR0LMwwLX6wsF8L8OFXm1mjOHb0czY89tjYkDQHBMKskZ2dQoZ019DxvOhj2pTXGwMKZnrbIAXIx40sw1t8ahGr7Olsw/fIfNpSxxGN3Mg9YVHfAPiePnYyEFJg5mRgY4W5kQ9q+JHy1MDBndzAMg36R64ukunztJtdr11R6kb1+/wubfl2NoZESDUthU8Orli3h4euc1x34sLjaWmVMnAtC2Q+78O8lJSYSFBlOtRv4EIT4ujo/fHw1Arz4vx8vBx9avW0NmZiYjBr6hN8+o2ioVyUObNrl/VDduqM/cmZWVRWBgIBYWFjg56X97eCMjIxYuXkr3Lp1o36YF/foPxMrami2bNxISHMyUadPxrVRJ12HqTJsWTXBzc6NylaqYmplx7swZjh09jLe3D6vW/vHCvZ3RZPbM6fy+ZhUWlpZUqOjLnO++USvTpXsPar4gtQnPo2O31/hr7W+c8T9Gt5Z1aN2hC9Y2tty+fhX/YwcxMTVl/JRvAbh/N5J+nZtRo059KvhWxtHZhbjYGM6fPkFIUACOzi58Mb3g5o766uCmddy6eAaA8ICbABza/DvXz+W2F67fuhMN2nQmOSGOzwd0xKdabdwrVsHG3oHYqPtcPHGI5Pg4ajZuSZc33tbZcRSHoY3cebWaM6mZOYTHpzGkoXoNs19QDEHRqWTlqFh4LIT/ta/Itz2rcTIkjrDYNOzMjWlewR5LUyM2XbqXb2jXad0q4+tsSWhsKlZmxrzZSH3/f124q5cTpfWpXZbWFR1Iy8rhXmIGvR8lR/90JiyB0Lg0rEyNmNWjKkHRKYTFpZOYno29uTG1Xa2xNjPi8t1Edlx/qLb94PrlsTLNfax4POHc4PrlSX/ULGzLlQfcTVSfZE8fbP1zFZfPnQLI63+w/c/VXDh1HICW7bvQskNXAEKDbrN6Se5gAxmPmv6F3glg+mdjALC1s+f9z7/O2/ecqeOJj42hVr2GuJRzIzsnm/A7gZw+fgiVSsXYCdMp5+ZRMgdahP5Yt5q1q36jWYtWuLl7Ym5uTkR4GPv37iIlOZmuPV6jd9+BAMTGxtC2WX1q161H1eo1cHR04v69uxzct4fY2BhatWnP6DEf6PiISlZpm9vhn0pF8lChQgU6duzI3r17Wbp0KSNHjsxb9+233xIfH8/gwYNLzVwPrVq34cBhP6ZPm8zff/1JZmYmVatVZ9KUrxk46A1dh6dTfV7vx9Ytmzhz+hRZWVl4enkz/vMv+eDjT585XfqLIjwsBICU5GTmFjD6hIen50uZPBgaGrJw9SbW/LaAPds2smvLX2RnZWLv6EyXXv0YMeYTfKvkvmkv7+rOiDGfcPbkMY4d2ktifBwmpmZ4eFdg1LjxDB75HrZ2Djo+oud36+IZjm7boLbscULhVM6NBm06Y2FtS8d+bxJw5QLnj+4jNTkRUzNz3CtWoXmX3rR9bSAGL1gyXtY69+2duYmhxsQB4H5iOkHRuQnBzmtR3E/MoE+dclQra0VjLzvSsnIIik5h59Uo9v5rdKbH+/e0N2dYY80DFOy+HqWXyYOTZW5TszLGhhoTB4CHyZmExqWRnJnNnpsPqehoQT13a8xNjMjIziE8Lp0/LsRyMCBGY61FI09bnCzzv0Ft5PVkOOQjgbF6mzxcPneKXZt+z7/s/Ckun89NKMq5uuclDzHRUWplY/+xrKyre77kYeBbYziydzvXL5/n+KG9KHNycHB2oV3X3rw+eCQ16jYszkMrNt169iYxMYHzZ05x8oQfaamp2NrZ07BxM/oNfINeffqjUOQ2D7Szs2f42+9y7swp9u7aQWJCPOYWFlStVoM+/QbxxptvvRQvBx87e+Y0169dpX6DhlSvUfPZG+gZhUr1vC0edSMoKIimTZsSFRVF165dqVKlChcuXODgwYN4enpy8uRJypbVfEH8t8RHIxo9iEl4aR5In0e2pmlGBYDGjpUi19240jNMpS5ce6jfnYx1afHRUF2HoLdcbF+MGb6Lw7imXroOQW9VcJHRsJ7GwvTlSVS0lZiYiKuzHQkJz342LhWjLUFu7cPZs2cZNmwY586dY968eQQEBDBmzBhOnz6tdeIghBBCCCGEKJzS0c7nEXd3d5YvX67rMIQQQgghhHgplZqaByGEEEIIIYRuSfIghBBCCCGE0IokD0IIIYQQQgitSPIghBBCCCGE0IokD0IIIYQQQgitSPIghBBCCCGE0IokD0IIIYQQQgitSPIghBBCCCGE0IokD0IIIYQQQgitSPIghBBCCCGE0IokD0IIIYQQQgitSPIghBBCCCGE0IokD0IIIYQQQgitSPIghBBCCCGE0IokD0IIIYQQQgitSPIghBBCCCGE0IokD0IIIYQQQgitSPIghBBCCCGE0IokD0IIIYQQQgitSPIghBBCCCGE0IokD0IIIYQQQgitSPIghBBCCCGE0IokD0IIIYQQQgitSPIghBBCCCGE0IokD0IIIYQQQgitSPIghBBCCCGE0IokD0IIIYQQQgitGOk6AKF/jAwlpyyIgUKh6xD0lqejua5D0Gvlbc10HYLeauhmr+sQ9NaPx0N0HYLeatdvoq5D0FtvTXxP1yHotUntfXUdgt5JzcjRuqw8JQohhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQitGug7gZXX2zBmmT5vMqZP+ZGZmUrVadd4f9yEDBg7SdWg6J+dGO3O/n8XELz8H4NDREzRs1FjHEZWMP35fw4njfly8cJ7rV6+QmZnJgiXLeGPIsGduGxISTNP6tUlJSWH4yFH8OH9h8QdcQu7djWTLpr/Yv3c3AbdvEfXgPnZ29jRs3JSxH35CvQaN8pX/7ptpzJ75tcZ9mZqaEhmdXBJhl5iW9aoQGR6mcd3AoSOY/v38vN8rOJs/c3/HLtymvKtbkcVX3JJjHhB4Yg8h544SF3mH1PhozCxtKFflFV557S3KVqqdVzYnO4vgM4cIPnOYB7cvkxR9D4VCgb17Baq06UWNjv0wMDTMt/+HwTcI8t9H2KUTJN6PICM1CUsHFzzrNqf+66OxdHAp6UP+Tz5+sz0zPuwFQKuh33P6SshTy3uWd+Dshi+wNDfl17/8GDdjvVoZM1NjRvVtwYAuDfBydUChgLB7sfy99wKL/jhCYnJ6MRzJf5Mc84Ag/z2Enj9G/KPvjamlDeUq16VurxG4VKqVVzYnO4uQM4cIOXuYBwFXSH70vbFzr0CV1j2p1kH9e6PJ9hnvEnb+KIbGJoxef6E4D6/YJMTHM+ubqVw8f5aw0BAS4uOwd3CkQsVKDH/7Hbr2eA2FQlHg9qEhwbRp+gqpKSkMHf42s378pQSjfzZJHnTg6JHDdO/SCRMTE/r2G4C1jQ1bNm9k+NA3CAsNYfznX+g6RJ2Rc6OdmzduMH3aZCwsLEhJSdF1OCVq+pRJhIWF4uDoSNmy5QgLC9VqO5VKxZhRI4o5Ot35ddEvzPthNt4+FWjdph2OTs7cCQpk5/Yt7Ny+hSXL19Crd1+17QYMGoK7p1e+ZUZGL+atwcrahuGjxqgtr1nnlXy/j/tU83UmNPgOW/5eT8VKVUpV4gBwacdazm9aik1ZdzxqN6WMjT3x98K4c+oAd04foNPHs/Ft9ioACffD2TXrQ4zLWOBWsxHeDduQmZJE8NnDHFnyNWEX/Og64ed8Dz+HFk3lQcAVXCrWwLfFqxgamfAg4DJXdq8n4MQe+sxYjb2bj64O/7lU9nZh4rtdSU7NwNLcVKttlkwd/NT1RkYG7Fkyjoa1vLl4M5y1206hUqlo2aASU9/vTr/O9WgxZDZp6VlFcQhF5srOtVzYvAzrsu641cr93iTcCyX49EGCzxykw4ezqdisMwCJ98PZ8/1Hud+bGo3wbtCajNRkQs8e5uiv0wm74Mern//81IfmGwc2En7RD0MTU1CpSuowi1xsTDS/r1lBvfqNeLVrD2zt7Ih++JC9u3cwcugABr85gu/naX55pVKp+HDM2yUc8fMpFXeIFStWMHz48KeWadu2LQcOHCihiAovOzubd0ePRKFQsO/gUerUrQvAlxMn07pFE76eOpneffpS0ddXx5GWPDk32snJyWHUyGHUrFWbir6VWL9uja5DKlHzFi6hQgVfPDw9mTv7O6ZO0i6hXLzgZ076H2fajO/44rNPijnKkvdKvQZs232IJs2a51vuf9yP3t078r8P3+fVrj0wNc3/MDRg8Js0b9GqJEPVGWsbGz4Y/9UzyxVUZsqEjwHo98awogyrRLj41qT3jFW4Vqufb3nk9bNsnvwWhxdNw6dhOwyNTTApY0Hr0ZOo0qYnxqZl8so2T09l48RhBJ85ROCJPfg+emgEqNyqO50+nIVNOY98+z+3cSknVs/Fb8Useny1qHgPsggYGChYOm0IV25HEhAaxaBuDZ+5zXsDW9Gktg9f/rSZWZ/20VimZ5vaNKzlzeYDFxn46dJ86/6Y8zY92tbmtfZ1Wbf9dJEcR1Fx9q1Jr69XUb5avXzL714/x9apb3FkyTS8G7bF0NgE4zIWtHx7IpX/9b3JevN/bJ40jJCzhwny30vFpp00flZyzH2Or5hFra5DuHNyH6nx0cV6bMXJw8ub22EP1V7EJCcl0aV9c9asXMbId9+nStXqatsuW/wLZ06eYOK0mUz+4n8lFfJzKRV9HurUqcPkyZM1/lSvnnviO3XS/GXUN4cPHeROUBD9BwzKezgGsLKy4vMvJpKdnc2qlct1GKHuyLnRztzvv+PK5UssXLIMQy2qgF80bdq2x8PT87m2CQoKZOqkL/jg4/9Rq06d4glMx7r1fE0tcQBo0qw5zVu2Ji4uluvXruggshdDRno6W//+AxMTE3r1HajrcJ5bxSYd1BIHANdq9XGr0Yj05ASiQ28DYOngQs3OA/I9AAIYm5lTt8ebAEReO5tvXe0ub6glDgB1ew7HyLQMd6+dKapDKVafDOtAzUqujJ6yhhyl8pnlfdwdmTa2B3NX7ufizYgCy3m7OQKw5/g1tXW7/XKXOdtZFTLq4lOhcQe1xAGgfLV6uFZvSEZyAjH/+N7UKOB7U6d77vfmad+DQ79MpIy1HY0GjivCI9ANQ0NDjTW4llZWtGnXEYCQO0Fq64ODApkx9SvGfPAJNWrVKe4wC61U1DzUqVOHOhpu+JmZmfz8888YGRnx5ptvlnxghXD0yGEA2nXoqLau/aNlx44eKcmQ9Iacm2e7du0q30yfxmcTvqRaNfU3FkKdUqlkzKgRuHt48tkXEzl9yl/XIZU4IyPjR/9Vv+SfPOHH+bNnMDQ0xLdSZVq1aadWO/GiyMzI4O/1a3hw/y42Nra80qAxVWvUevaGwJ4dW0iIj+PV7q/h4OhUzJGWLANDo0f/ffbLiOcpC6BQKFAYGKBQ6P+7ymoVyvHl6Ff5dukebty5/8zyCoWCJVMGE3Yvlm+W7KJRLe8Cy14PugdAx6bVWLEp/zWoU7NqKJVKjp4L+G8HUMIMHl1XtPreGD39e3Nt75+EX/an59TlGJmaFV2QeiY9PR2/o4dQKBRUqlI13zqlUsmHY97Gzd2Djz/7irOnT+ooymcrFclDQTZt2kRMTAy9evXCxaV0dMYKDMy9OFSsqN70xs7ODkdHR4ICS9cFpKjIuXm67OxsRo8cTuUqVfnkf5/rOpxSY8H8nzh18gR7Dhx9YR+KnyYiPIyjhw/g7FKWatVrqq3/dvqUfL+7lC3HL4t/o3Xb9iUUYcl5GPWA8eNG5VvWsm0H5vyyDHsHx6du++e6lUDpbLL0NEkP7xJ+2R9zO0ccPCo9s/z1AxsB8KjTVKv9B/rvISstpcCmKvrC0NCAJdOGcDP4Ad8v36vVNmPfaEPj2j60e+sHMrOyn1p259GrbD9yhdfa1+X42vH4nQ8EoEU9Xyq4O/LBzD85f11zh359lPTwLhGX/TG3dcRei+/NjQObAHCv3Ux9X1F3ObHqe6p37Idr9QZFHqsuJcTHs2ThPJRKJTEPH3Jg324iI8L55POv8KmQ/1lnyYJ5nDnlz9Y9h/X+XlWqk4dly5YBMHLkSB1Hor3EhAQAbGxsNK63srYmMqLgqs8XmZybp5v93TdcuXyJw34nMTY21nU4pUJgwG2mT53Iu2PG0bBxE12HU+KysrJ49+1hZGRkMOXrmfmaudWsWZtfFv9G0+YtcXJ24W5kBJv+/pMfv/+Wwf1fY/dBP2rUrP2UvZcufQcOpWHTFvhWroqJqSmBt24w7/tvOHJgL6OG9GXDjoMFduQMDw3hpN8Ryru507x1uxKOvPjkZGex98fPycnKpNnQT5/5Bvnq3j8JPX8Mt5qN8Kr37H4ySdH3OLr0G4xMzGg8SL+boowf0ZFavq60HDqb7OxnN1eq6OHM5Pe68cvvhzl1OVirz+j/8RKmj+vJB0Pa8kq1J0281m4/xd7j1wsde0nLyc7iwLwJ5GRl0mTIJ8/83lzb+ydhF47hWrMRnvVa5lunUqk4tGAiphbWNBny4vVFS0iIZ8630/N+NzY2ZtLX3/Lu2I/ylQsKvM130yfz9rtjqd9Q/0dOLLXJQ2hoKAcOHMDV1ZXOnTs/ewMhSrHLly/x3cwZfPDRJ9St+8qzNxAolUreffstypYrz1dTNA9J+iJTKpWMe28k/sePMWTYCPoNzD8aTJfuPfP97lOhIp+M/wInJ2c+Hvcuc2fN5LfV6sNNllZj/zWCUp16DVm6diMDe3bk7KkTHN6/mzYdXtW47YbfV6JSqXh9wBAMDPS/+Y02VEolB37+irvXz1K9Q1+qtO7x1PLBZ49w5NfpWDmVp+OHs565//SkeLZ9/Q6pCbF0GDcTO9eCm/ToWs1Krnw+sjM/rj7w1H4LjykUCn6dNph7DxOY8ss2rT7DzNSYVd8Op0ENL4Z9sZIDJ2+iUqlo07Ay349/nY7NqtFq6ByCI/S7k7BKqeTQLxO5e/0s1dq/TuVnfG9Czh3h2LIZWDmVp/0H36mtv7ZnPRFXTtLtq0WYlLEorrB1xsPTi/sJmeTk5BAZEc6Wv//k268ncfaUP0tW/o6RkRFKpZIP3h2JS9nyfPbVVF2HrJVSexVcvnw5SqWS4cOHP7PTaEZGBomJifl+dMX60Vv1hEdv2f8tKTGxwDfvLzo5NwUbNWIYPj4V+HLiFF2HUmos+mU+Z06fZP7CJZibP3vc/heJSqXio/dHs2H9OvoOGMScnxZove2AN4ZiZGTEqZMnijFC/WBgYMDrA4cAcO605r4wSqWSv9evwcDAgL6DSkffumdRqVQcXDCJW0e2UblVd9q8M/mp5UMv+LFr1geY2zjy2rTlWNg/vc9HenICm6eMICY8kDajJz0zMdG1X6cN4U54NNMX7dSq/JiBrWhY04t3p63TemjV/73Vke6ta/H+9N/ZsOccsQkpxCWmsnH/BUZPWYOTnRVfjNKcvOoLlUrFoYWTuX10G5VadqfV6Kd/b8Iu+LFn9oeUsXGkx5TfsLDL/71JjnmA/+q5VGnTC4+6LYozdJ0zNDTEw9OLsR+P57OvprJz+xbWrMhtPbN00c+cO3OKOfMXlZp7VamseVAqlSxfvhyFQsFbb731zPIzZ85k6lT9yOYet+cPDAzglXr5RzCIi4sjOjqaxk20a0v6opFzU7Arly8BYG9dRuP6Ni1zz8v6PzfSvWevkgpLr125fBGVSkW3TpqbmSxfuoTlS5fQtVsP1m3YVMLRFZ/cTnejWLdmJb37DuDnRb8919tyExMTLK2sSEtLLcYo9YedvQMAaWlpGtcfObiX+3cjadGmPeXd3EsytGKhUio58MtEbhzcRKUWXWk/9hsUT/l+hJ4/xo7vxmFmbcdrXy/HpuzTz0F6Ujybp4zg4Z0btBo1kRqd+hf1IRS52pVz5+xIOP2jxvVHVn0KQL+PlrDt8GVqVXbDwMCAvUs/0Fj+7deb8/brzdl26BL9Pv4VgM7Ncwe4OHLmtvr+zwSgVCqpW1V/v18qpZJDCydx8+AmfJt3oe37M576vQm7cIxdsz7AzMqOXlN/0/i9SbgXSlZ6KjcPbebmoc0a97OgT+55G7HKH1ML6yI5Fl1r3bY9X0+awAm/IwwbOZqrVy6hUqno062DxvKrlv/KquW/0rlrd1as+7uEo9WsVCYP+/btIywsjHbt2uHt/eyq0AkTJvDxxx/n/Z6YmIi7u27+SFu0bMXs72ZyYN9e+vUfkG/d/n1788q8jOTcFOzN4ZqT5OPHjhEYGEDXbj1wdHLEw8urZAPTY81atMRQw+hCD+7fY+/uXVSqXIVGTZpSu3ZdDVuXTv9MHHr16cfCX1c893C+QYEBxMfFUaOmdqMQlXYXz+cOHenmrnn43w1rcztK9x/89LmGSoN/Jg6+zV+lwwffPrW9euj5Y+z4dixmljb0nrYc23JPHyL5n4lDy7e/pNarpWNI2+WbNNeyNX+lIr6ezmw7fJnouGRC78YAcOxcINk56v0iyjpa82qLGty8cx//S3e49I8mUCbGudciRztLklMz8m3naGuBgYEBGZlP73StK/9MHCo2e5V2457+vQm7cIxd343D1NKGnlOXY1PA98bczomq7TTPixF4fBfZmRlUadMLAEMjk/98HPri/v3ckbcej37XpFlLjAw13Kse3OfA3l34VqpMg0ZNqVG7TkmG+VSlMnl43o7SpqametNzvU3bdnj7+PDH+nW89/44aj8agjYpKYlvv/kaIyMjhgwdptMYdUXOTcEWLFqqcfmokcMJDAzg0/Gf07CR/neyKkmDhw5n8FD1B75jRw+zd/cumrVoyY/zNc/wWRoplUo+eO9tfl+7ip6vvc6ipSsLTBySkpIICw2m+r+GKY2Pi+ODMbmjEfV+fYCmTUulgFs3cClbDmsb23zLz548wW+L5mNiakqnrj3VtouJfsjBvTuxd3CkXaeuJRRt8chNHL7ixsHNVGzaiY4ffqdV4mBqac1rX6/AtrzXU/efnhTPpslvER18k5YjJlC7yxtFfATF571p6zQuXzJ1ML6eznz/215OXwnJW75660lWb1UfRrNFPV9ebVGDY+cDGTcjf38h/0t3qOFbni9Hd2H0lDUolbmzJysUCia+l/vdOnpW/0YTVCmVHFowkZuHNlOhSSfaPyPhzEscLKzpOXU5tuULTjjtXL1p8940jesiLvuTGh9d4Hp9d/XyRTw8vfOaYz8WFxvLzKkTAWjbIXcEsoGD32TgYPUmkcePHeHA3l00adaSWT/+UvxBP4dSlzzExMSwZcsW7O3tee2113QdznMzMjJi4eKldO/SifZtWtCv/0CsrK3ZsnkjIcHBTJk2Hd9Kzx727EUk50ZoY+XypZw8cRzInfcCYNXy3/B7NAdI1+496dajl67C05nZ307n97WrsLC0pEJFX+bM+katTJduPahZqw5xsTG0alKPOq/Uo1q1Gjg6OXPvXiQH9u4hNjaG1m3b8877mptklEY7t/zNkl9+oGmL1ri6e2JiYsrtm9fwO3wAAwMDvp49T2OTpE1/riMrK4vX+g7CxKR0v/k8/ecCbhzcjLGZObblvTizQX22Z59G7XDyrkpsxB12fDuWnKxMXKs35PaxHWplrZ1dqdr2yT1453cfEB18EztXH9KTEzi1/me1bep0H/rCND15XrOW7qZbq5oM7t6IulXdOXLmNiqVipb1K1GzkishkdHMWbFP12GqObNhITcPPf7eeHL2r8VqZXwatsXRuypxEXfY9d04crIyKV+9AQF+6n1IrJ3KU6Vt6Xt2e15/rFvN2lW/0axFK9zcPTE3NyciPIz9e3eRkpxM1x6v0bsUTjb5WKlLHlavXk1mZiaDBw/Wm9qE59WqdRsOHPZj+rTJ/P3Xn2RmZlK1WnUmTfmagYNKz9ua4iDnRjzLyRPHWbdmVf5l/sc56Z+bUHh4er2UyUN4aAgAKcnJzJ09U2MZDw9Pataqg52dPSNGvcvZM6fYs2sHCQnxmFtYUK1aDV4fMIghb454oWYvb9y8FYEBt7h2+SKnT/iRkZGOg5MzXXu9zluj36f2K5rHlt/weG6HwcNKMNrikRh1F4Cs9FSND4CQmxA4eVclNT6anKxMAI0PgACu1RvkSx4SoyIBiIu8w+k/NHfQr9r2tZc2eYh4EE+Tgd8yfkQnOjarxog+zVCpIOxeLD+tPsCsZXuJTUjRdZhqkh79f81KT+Xc30s0lrF2Ko/jv743gcd3aSxbvnqDlyJ56NazN4mJCZw/c4qTJ/xIS03F1s6eho2b0W/gG/Tq07/AoaFLA4VKpVLpOojnUbNmTa5evcrly5epWVN9wiNtJD4atedBTALW1i/nhUwUzuOqZqEuW87NU2VpMXb8yyohTbsRa15GPx4P0XUIeuvXqfrVlEOfvDXxPV2HoNcmtVefjPZll5SYiK+7IwkJz342LlVDtZ4+fZqrV6/SsGHDQicOQgghhBBCiMIpVc2WGjZsSCmrKBFCCCGEEOKFUapqHoQQQgghhBC6I8mDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0YqTrAIQoTZQqla5D0FsPEzN0HYJe+/lkqK5D0FvnAqN1HYLequRmq+sQ9Nbhv6brOgS95WZfRtch6DVTY3l3/m8Zz3FO5OwJIYQQQgghtCLJgxBCCCGEEEIrkjwIIYQQQgghtCLJgxBCCCGEEEIrkjwIIYQQQgghtCLJgxBCCCGEEEIrkjwIIYQQQgghtCLJgxBCCCGEEEIrkjwIIYQQQgghtCLJgxBCCCGEEEIrkjwIIYQQQgghtCLJgxBCCCGEEEIrkjwIIYQQQgghtCLJgxBCCCGEEEIrkjwIIYQQQgghtCLJgxBCCCGEEEIrkjwIIYQQQgghtCLJgxBCCCGEEEIrkjwIIYQQQgghtCLJgxBCCCGEEEIrkjwIIYQQQgghtCLJgxBCCCGEEEIrkjwIIYQQQgghtCLJgxBCCCGEEEIrkjwIIYQQQgghtCLJgxBCCCGEEEIrRroO4GV19swZpk+bzKmT/mRmZlK1WnXeH/chAwYO0nVoOvX72jUc9zvGhfPnuHr1CpmZmSxZupwhbw7TdWg6t2bVCt4dNeKpZVq1bsv23ftKKCLd2rtzC2uX/8q1KxdJS0vFycmFOvUaMn7SDMq7ugFw/eoldm75m6uXL3LtykXiYqJp2LQF6zbt0XH0hZcc84Dbx3cTcu4ocRHBpMRHY2ZpQ/mqdan32gjKVa6dr3zUnRsEHN/Ng8BrPLxznbTEOFxrNKDvjFUFfkZ2RjqXd6/nxuGtJD6IRIUKa6fyVGrWmdpd38DUwqq4D7NQHC1NaOPrSGNvOzzsy2BvYUJiejZX7yay7kwEN+4nq23jZmvG4Ibu1HK1xskqt3xITCobL9zj+J3YfGUndPLl1eouT41h6fFQVp0KL9LjKgq2ZYyo72ZDzXJWlLUywcbMiJTMHAJjUtl9M5rg2LR85Vv42FGnvBXlrc2wNjMiR6kiJjWTi5FJ7A+IISUz55mfOa65J7XKW5GVo+Tdv68X16EViV2b/+DSWX9uXr1E0K3rZGVl8tV3v9Ctj+Z7ckpSIr/O+47De7YS8zAKBydnWnfqwdvjPsPCyjpf2Wnj32Pnxt+f+vmjPvyCt97/X5Edjy4smDeHb6Z8CcCWPUeo16BR3rqsrCz27trO/j07uHD2DJGR4RgYGOBbqSp9Bw5m8LCRGBoa6ir0YvHH72vxP+7HpQvnuX4t93nml8XLGDTkTbWydubPfhy/cjsYNzf34gj1uUnyoANHjxyme5dOmJiY0LffAKxtbNiyeSPDh75BWGgI4z//Qtch6syUyV8RFhqKo6MjZcuVIyw0VNch6Y1atesw4ctJGtdt3vQ3N65fo12HjiUcVclTqVRM/N9Y1q/+DQ8vH7r2eh0LCyuiHtzjtP8x7kaE5SUP+3ZtY9G87zE2McHbx5e4mGgdR//fXdy+hrMbl2JT1gOPOk0oY+NA/N1Qgk4dIOjUAV795HsqNX81r3zQqQOc+WsJhkbG2Lp6kZYY99T952Rn8ddXb3L/9mWcvKtStW1PFCgIv3qaE2t/4taxHQz4/k+MTcsU96E+tz51yvFGQ3ci4tM4GxZPXGoWbrZlaF7RgeYVHJi28xaHbj/5DlQta8lPfWtiZKDg+J1YjgREY2tuQsuKDszsZcdvJ0JZcfJJInAsMIb7iRkaP7t/PVfMTQw5E/r086sr7So68GpVJ6KSMrj+IIWkjGycLU2o62pN3fLWLDkZztmIxLzyTTxtMTc2JCA6hYT0bIwMFPg4mNO9ujNNvWyZceAOienZBX5eM29bapS1JDNbiUJREkf43yz+YQb3I8OxtXPAwdmF+5EFJ4BpqSm8O6gbt29coWGzNnTo1oeAm1dZv3wB508eY/EfuyhjbpFXvlWHrpRz9dC4r3XLfiYtNYVGLdoW+TGVpIBbN5kzcxrmFhakpqSorQ8NvsPoYQOxtLSiWcvWdHi1G0mJCezbs5Mv//cBhw/s5be1f6EoDV8WLc2YOonwsFAcHB1xKVuO8LCCn2c++2KixuV37gSxYf06KlepqjeJA5Si5EGlUrFp0ybmz5/PzZs3SUhIwN3dndatW/PZZ5/h4+Oj6xC1kp2dzbujR6JQKNh38Ch16tYF4MuJk2ndoglfT51M7z59qejrq+NIdWPh4qVUqOiLp6cns2d9y6QvJ+g6JL1Rq3YdatWuo7Y8MzOTxYt+wcjIiDcGDy35wErYqqULWb/6Nwa/NZqJ079Xe1uVnf3kgebV7r1p16krlavWID4uhiY1S8d14mnKVqpJ329W41q9fr7lkdfO8vektzi4aCo+jdphZGwCQKVmnajQsA0OnpVIT4rn12Etn7r/oJP7uX/7MhWbdKDb5/Pyrdv2zfsEnTpAwPE9VGvbq0iPqyjcuJ/M+39c5nJkYr7ltVyt+eH1GnzcrgJ+QTFk5agAGN7EAzNjQyZsvp6vlmG5fxgrhtZlUAM31p6JyCvvFxSLX1D+2giASs4WDG/iQdDDFI21G/ogODaN7w7eISA6Nd9yX0dzPmnlxeB65bl4N4lsZe6xzj0Skvfvf+pZw5nu1ZzpWMmBvy4/0PhZdmWM6F+7HPsDYnjFzRobM/1/1Pjim3m4e/lQztWDVYt+YMH30wosu2bJPG7fuMLgUeN4f/zUvOW//jiTZT/PYs2Sebz94ZN7V6sOXWnVoavafm5evciy+d9RoXI1qteuV7QHVIJycnL4cMxIqlavhU/Fimz8U72WxcLSkm++n0ffAYMpY26et3xSSgp9e3Rk3+4d7NiykW69+pRk6MVq3oLF+FT0xcPDkx++/45pk74ssOznX03WuHz8xx8AMGTYW8USY2GVmj4Pn376KX369OHWrVv06tWLsWPH4u3tza+//kqdOnW4evWqrkPUyuFDB7kTFET/AYPyEgcAKysrPv9iItnZ2axauVyHEepW23bt8fT01HUYpcq2LZuIjYmhc5euOLs8vUlFaZeelsb8Od/g7unNV1/P1ljNbWT05EGlUpVq1KhVF2Nj45IMs1hVbNJRLXEAcK1eH7caDUlPSiAm5HbecgcPX5wrVMfQSLtzkHA/AgCvV1qorfOqn5t4pCWoP0Drg6OBMWqJA8DlyEQuhCdgU8YYH8cnb4TL2ZihVKk4FZK/tiAqKYPg6FTMjA0xN352U4puNcsCsOOq5odpfXA+MlEtcQAIiE7l5sMULE2NcLUxzVuuKXEAOBeeAICzpanG9QBvNnAlKSObTXp8Pv6tYbPWBdYO/JNKpWLrhtWYW1gy4v3x+dYNffcjrG1s2bphDSqV5vP3T1v+zG062KPvkMIFrScW/PQ9N65eZs78xQU2PSpX3pWhb43KlzgAmFtY8PZ74wDwP3Gs2GMtSa3btsfDo/DPM+np6Wz4Yx0mJib0Hzi4CCP770pF8nD//n1+/PFHvLy8uHnzJgsXLuS7775j9+7dzJ07l6SkJObOnavrMLVy9MhhAI3NS9o/Wnbs6JGSDEmUcqtW/AbAm8Of3h/iReB35ADxcbF0eLU7OTk57NmxmUXzvmfdyl8JCQ7SdXg6Z/gocVL8h7bDDh4VAQg576e2LuTcMVAocKvRsND715XHD8M5/3goDolJxUChoIGnbb6yzpYmeDuaE/Qwt8nO05gYGdCushMZ2Ur23Igq8rhLwuNzUkC+kE/Ncrn9XSIT0zWub+ljRzUXS1aejcyrsXmRhIcE8fDBPWq90ihf0yQAU1Mz6jRoysMHdwkPvfPU/aSnp7Fv29+YmJjSuVf/4gy5WN28fo0fZs1g3CefU7lqtULtw+jRiw0jQ/2voSpJ27ZsIj4ujle7dsfRyUnX4eRTKv5PhYSEoFQqadasGdbW+Tside3alY8++oioqNJx0Q4MDACgYkX1Zkl2dnY4OjoS9KiMEM8SFhrK4UMHKe/qSoeOnXUdTrG7euk8kFu70L1tI+4EPnnDbmBgwPDRY5kwZaauwtOpxId3Cbvkj7mdI46elQq9H+8GrfFp0IZA/72s+7gPrtUbABBx9TQJ98JoO3oSLr41iirsEuFsZUo9D1tikjO5E/2kPfbS46HULG/N9B5V8QuKJTI+DdsyxrT0deBBYgaTt9985r5b+zpgZWbE/psPSXpGoqGP7M2NqeZiSXxaFhEJ6glBUy9bHC1MMDMywMPOjCrOloTGpbHvlnr/IXtzY/rWLsuRoFhuP1Sv5XgRhIfkvqRw89LcBNL90fKIkCA8vCoUuJ9Du7aQnJRIh269sbG1K/pAS0B2djYfv/82FStVYcyHhe/s/cfalQC0bNu+qEJ7IaxZmftiUN+aLEEpSR58fX0xMTHh+PHjJCUlYWX1ZKSPnTt3AtC2benobJSYkFvla2Njo3G9lbU1kRERJRmSKMXWrFqBUqlk8JBhL9xIFZrERD8EYNnCn6hesw4bdx+lQqUqXL9yia8+fZ9lC3/Cw8ubN4aN0nGkJSsnO4s9P3xGTlYmLd78FIP/8F1QKBR0mzCf46vmcm7LcqKCnoySU7V1Dzw1NGfSZ4YGCr56tRKmRgbMPhac7+16aGwa7/5+iandqtCmkmPe8sT0LHZee0BEfJqGPebXtUZuk6XtV+4XeezFzVABIxq6YWxowF+X76KppU0zLzsqOz95w371fhLLTkWQmqVUKzusgSupmTkF9oV4ESQn5TaLs/zXiEqPWVha5ytXkK0b1gDQo1/p7ac2f+53XL96mW37jhW6aeiaFUs5tH8PzVq2pl2HF/8FmLZCQ4I5duQwbu4etGnXQdfhqCkVyYODgwMzZszgf//7H1WrVqVHjx5YWVlx5coV9u/fz6hRoxg7dqyuwxSiRCmVStasWoFCoWDIm8N1HU6JUCpzH1iMTUxYuPIPXMqWB6BB42bMX7qWbm0asmzhvJcqeVApleyb/yWR185So2Nfqrbp+Z/2l52Rzs7vP+H+7cu8+vFsPOo0RaFQEHb5JId/nUHI+WP0n7Ue23LPbh+uawrg846+1HGzYevl++y98TDf+soulnzTsyoh0amMXHOB0Ng07M2N6VWnHGNb+1Db1ZqvthVc++Bqa0ZtN2vuxqdz/lFfgNJCAQxr4EZlZwuOBMVyMjReY7nZh4MBsDQxxNuhDK/XKsvEDhWZdyyEiIQnI0+1rmBPNRdLfjgaQka2emIhnggPucPFMyco7+5J/SZPH8BAX12/epl5c75l9PsfUrN23WdvoMGBvbuY+NlHuLl7MG/Ry9vXU5M1K5ejUql4Y8ibGBjoXw+DUpE8QG6H6fLlyzN69GgWLlyYt7xp06YMHjz4qVlvRkYGGRlPLnKJiU9/I1CcrB/VOCQkaL7RJCUmFlgrIcQ/HTywj/DwMFq3aYeXt7euwykRVta5fxs1a7+Slzg8VqlKNdw9vQkNDiIxIR5rG1sdRFiyVCoV+3+ZyM3D26jSujvt3p3yn/d55q8l3Dl9kO5f/EKFRk9qdCs164xJGQs2Tx3FqT8W0OnDb//zZxW38R0r0qmaM3uuRzFnf2C+dYYGCqZ0rYxKBV9svZH3wHsvMYOFR0NwtjSlXRUn6rrbcKGAxKBrDRcMFAp2Xit9b9qH1neliZct/iHxrDl395nlkzNzuHIvmYj4UL7p4svQ+q58cyC3Xb9tGSNer+XC8eA4runpaFNF5XGNQ0E1CynJT6+ZANi2YTUqlYpurw8utUOTfvjeSDy9fPj4M81DjD7L4QP7GPXmABydnPljy25cypYr4ghLL6VSybo1qzAwMOANPX0xqH/pTAGmT5/OsGHDmDBhAuHh4SQnJ+Pn50d2djZt2rRh48aNBW47c+ZMbGxs8n7c3XU3Vu7jvg6BGvo1xMXFER0dTQUN/SGE+LcnHaX1rz1kcfGukPu38TiJ+LfHyXl6+rObm5R2uTUOX3Ft/0Yqt+xKx3EzURTBG6rgc7kDNrjXbKS2zq1mI1AoiAq69p8/pzgpgM86+tK1Rln23Yxi5p7b/LtFjqd9GVxty3DjfpLGN+Xnw+MBqOxsqfEzDBTQuZoz2UpVqUoecmscXGnhY8ep0Hh+OxOhdm6eJi4ti3uJGXjZl8HEMPfB18XSFDNjQ5p527G0X418P44WJhgbGuT9Xsa41Dx2aOT+qB9DRIjmDtHhj5a7FdDfIScnh52b1mNoaFjgBHSlwfWrlwkMuEWFcja42Zvl/Wz4Pbc5Vs9OrXCzN2P3jq1q2x7av5cRQ/piZ+/In1v34llA/5GX1f69u7kbGUGbtu1xd9fPGt5SUfNw8OBBJk6cyEcffcQXXzyZQK1Zs2Zs374dHx8fPvroI3r37q1x+wkTJvDxxx/n/Z6YmKizBKJFy1bM/m4mB/btpV//AfnW7d+3N6+MEE8TExPDjm1bsbO3p3vP13QdTolp3Dz3byMo4JbauqysLEKD72BuboG9g36NTFHUVEol+36eyPUDG6nU/FU6ffjdf+rn8E852VkApCXGYvKv0WTSE+NApcLw0RwS+kgBjO/oS9caLhy4+ZAZu25rHEXIyCD3wde2jOZa68fLM3M0N8Fp7G2Po6UpJ+7EEp2cWSSxFzcFucOoNve243RYPEtPR2js5/AsNmZGoHoyOlN8ehbH7mgevreBuw3GhgaceDQcbkFDwJYW7l4VcHIpx+Xzp0hLTck34lJGRjoXz5zAyaUc7p6aH4hPHN5LdNR9mrXpiPO/ak9LkwGDh2lcfsrfj+CgQDq+2g17B0fc/zVU6aH9exk5pC+2dvb8uXUP3j4Fdyp/Wa15NFz/ED0eQbFUJA87duwAoE2bNmrrnJycqFmzJv7+/kRHR+Po6KhWxtTUFFPTgsekLklt2rbD28eHP9av4733x1G7Th0AkpKSy2vFYAAAPL5JREFU+PabrzEyMmLI0GE6jVHov/Xr1pCZmcmIgW/ozXe7JHh6+dC8dXv8Du/njzXL6T/4SZXu4vnfk5gQT8/XB+ab6+FF87jG4frBTfg260znj2cVWeIAUL7KK8SEBnBy/S90GDsjb98qpRL/dbmTxrlpqJXQB49rHLrUcOHgrYdM33WrwOFHg2NSSUrPpkZ5axp42nLmH23+HSxM6FUntxnFxYiCmyyBfs/t8E+PaxyaedtxJjyBpacKThwsTAyxMTPirobZtHtUd8amjDHXHyTnJQIPkjJZeVZz06eqLpbYmCkKXF/aKBQKevQdwrKfZ7Hs51n5JolbtfAHEhPiGTFkVIHNkbY96ijdvZTP7fD9vEUal380ZiTBQYGM+fB/1GuQ/zrxOHGwsbXjzy178KlQsSRCLVWiHz5k987tODg68mrX7roOp0Cl4g6bmZn7Vufhw4ca1z9eXhoeooyMjFi4eCndu3SifZsW9Os/ECtra7Zs3khIcDBTpk3Ht1Lhh1ks7ZYvW8qJ47njy1+7eiV32W9L8+bH6N6zFz169tJRdPrjZZrb4d+mfvcj/bq25ctPxrB/1zZ8fCtz/col/P0O4+ruweeTZ+SVDQq4xeL5c4DcCeYA7gTcYvy43A7VdvYOpW5o15N/LOD6wU0Ym5ljV96TU3+q38QrNGqHs09VAGIj7nDm71+B3M7QAHERwez5KXcG3DLWdrQc/mSyq4Z9R3Pn9EFuHNpCVNA13Gs2BoWCiCuniA69jbWzK/V7jyzuwyyUYU086FLDhdTMbCLi0hjaWL3K/1hgDIEPU8jKUbHgaDCfdfRl1mvVORkcS2hsGnbmxrSo6IClqREbL97ljoaJ1ezMjWnibUdMSiYngmJK4tD+s+7VnWnmbUd6Vg4PkjLoVs1ZrcyFyETC49OxNzdmcseK3IlJ5V5iBgnp2ViaGuLraEE5a1Pi07JYe/7FSAYe2/LHKi6dOwlA0K3cEca2/rmK86dy70f/nCV68KhxHDuwK3em6WtXqFKjNgE3r+J/ZD+VqtZk8KhxGj8jJjqK44f3Yu/oTPO2L9fIQoG3bzFySF8yMjJo0qwFm//+Q62Mu4cn/QaV3tGn/m3V8mWc9D8OwPVHExmvWrEMv2O5TUO7dutJ1x75B7hYv241WVlZDBg0GBMT/a3hLRXJQ7Nmzfj555+ZO3cuffr0ydeheOXKlQQGBlKvXr18Q7jqs1at23DgsB/Tp03m77/+JDMzk6rVqjNpytcMHPSGrsPTqRPH/VizemW+Zf4njuN/IvcP0NPL66VPHs6eOc31a1ep36Ah1WvU1HU4Jc7Ty4dNe/34adY0jh7ch9+RAzg6uzD4rdGM/XgCDk5PHooeRj1g4x9r8m0f/TAqb5mru0epSx4SoyIByEpP5fSGxRrLWDu75iUPKXHR3Di4Od/61Pgny6ycy+dLHqycyjHoh785s2EJIeePcmXPH6BQYO1Unld6DqPB66MoY62f49KXtc59gWRuYqQxcQC4l5BO4MPcuR52XH3AvYR0+r7iSrVyVjTytic9K4eghylsv/KgwEnfOldzxsjQgD3Xoygt86A5mOc2wzIzNtSYOABEp2QSHp9OTEomO248pLKTBTXLWWFuYkh2jpIHyZlsux7F/tsxpGTmlGT4xe7SuZPs3Ph7vmWXz53i8rlTAJRz9chLHsqYW7Bg3TaWzpvFod1bOH/aDwdHZwYMf4+R48arTR732M6Nv5OTnU2X3gNe6NpRTaKi7ucNXLNl4waNZRo3a/FCJQ8n/Y/z+5pV+Zad8j/BKf8TAHh4eKolD3lNlobp94tBhUqbOdR1LCcnh/bt23P48GGcnJzo0aMHdnZ2XLp0iX379mFqasr+/ftp3ry5VvtLfDSi0YOYBLVJ54R4muwC2j8LeJCg3sRBPPHzyVBdh6C3zgWqTzgmclVys9V1CHrr7Xpuug5Bb7nZl9F1CHrNwuzlSt60kZiYiGdZexISnv1sXCrOnqGhIbt37+ann37ijz/+4PfffyczMxMXFxcGDRrEhAkTqFGjdM14KoQQQgghRGlTKpIHyO3PMH78eMaPH//swkIIIYQQQogiV7oHXBZCCCGEEEKUGEkehBBCCCGEEFqR5EEIIYQQQgihFUkehBBCCCGEEFqR5EEIIYQQQgihFUkehBBCCCGEEFqR5EEIIYQQQgihFUkehBBCCCGEEFqR5EEIIYQQQgihFUkehBBCCCGEEFqR5EEIIYQQQgihFUkehBBCCCGEEFqR5EEIIYQQQgihFUkehBBCCCGEEFqR5EEIIYQQQgihFUkehBBCCCGEEFqR5EEIIYQQQgihFUkehBBCCCGEEFqR5EEIIYQQQgihFUkehBBCCCGEEFqR5EEIIYQQQgihFUkehBBCCCGEEFqR5EEIIYQQQgihFUkehBBCCCGEEFqR5EEIIYQQQgihFUkehBBCCCGEEFqR5EEIIYQQQgihFUkehBBCCCGEEFox0nUAQpQmBgqFrkPQW87WproOQa992sJb1yHorexmXroOQW/lKFW6DkFvjfnrsq5D0FuH913VdQh6bc6n7XQdgt5JS0nSuqzUPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEIIIYQQQiuSPAghhBBCCCG0IsmDEEL8v737jori6t8A/uxSlr5UQRDBikg0FtSowV4wGkusWCL2GDHqL/paokGNUWNiXn1jjBp7j7FHQ8SGXYzRYMVKUYyAKFX63t8fhNV1F1iNMEt8Pudwjt65MzxzHXC+O3NniIiISC8sHoiIiIiISC8sHoiIiIiISC8sHoiIiIiISC8sHoiIiIiISC8sHoiIiIiISC8sHoiIiIiISC8sHoiIiIiISC8sHoiIiIiISC8sHoiIiIiISC8sHoiIiIiISC/GUgd4E23ZtBGnTp7AxQt/4MqVy8jJycGKlWswaHCg1NEkx7HR37ffLMCMz6YAAI4eP43GTd6ROFHZ2LplI86cOok/L17A1b+PkR9WrMKAQYEa/XJzc/Hrvr0I+XUfzv9+DvfvxUIul8OrVm30H/ghhg4fCSMjI2l2oows/d9CzJ35GQBgz4FjaNioicbyTetW4eBv+3Hj+lU8epQIYyNjVKrsgQ6dumD46LGws7OXInapaF7fC3H3YnUu6z94OOYu/A5AwXFz6Ld9OHTgV/z5x+94EHcPcpkc1b1qoVe/geg/ePi/7rhp0bBWkWMT8OEwzPnmO422qLu38cOiBTgffgYP/4qDra0dqtf0xofDP0I7/y5lEfm1crA0gV81BzSqbItKtmawszBBWnYerj1Mx/aLD3AjIUOjf8joJkVs6ZlB6y/iUUaORlurGg7oXscFHvbmyFUJRD5Mx4bf7+NWYkYRW5Ge0sIUnwc0QMPqTvCoYAU7KwWSUrNw80EKlodcx+6z0VrrNKrhhEk930bTWs6wNjdBbGI6fj55F1/vjEBWTr5GX3NTI4z090b9qo6oV9UBNVyVkMtl8Br1E2IT08toL1/duQO7cDvid9yLvIIHd28gLzcHA6d9jaade2n1HdO8Sonbm7PzFOycXQEA6+dMRHjIjmL7dxk+AZ2GfPJq4f8BFg8SmBk8HbExMXB0dIRLxYqIjYmROpLB4NjoJ/L6dcyZHQxLS0tkZBjufzylYc7MzxEbGwMHR0e4uFREbKzuYyTq7h0M6t8H1tbWaNGyNd7r/D5SU1MQsn8fPh0fhEMHf8PWn3dDJpOV8R6UjVs3IrFw3mxYWFriaRHHyM5tm5GcnIzGTZujgnNF5GRn48L5c1j8zTxs37oRvxw8gQrOLmWcvPRY2ygxdFSQVnvdeg3Uf46JvovRQ/rDysoaTf1aop1/Z6SlpuLwgf2Y8Z/xOHb4IH7c+PO/7rixtlFiyMgxWu11nhsbAPjzj3MY8EEn5OXmom3HzvDv0h1JjxJwYP9ejPqwD8ZN+gyfTPqsrGK/Fl3fckGfBq54kJKFi/dTkJyZBzelGZpWsUNTTzt8deg2Ttx5rO6/8ff7OrfjqjRDm5qOiH2cqVU49G3gisAm7ohPy8b+qwkwN5GjZXUHLOxRG5/ti8TlB2mluo+vysFGgQ/b1MS5mwn45VwMnqRnw0lpjvd8K2PLf9piVWgkgpadUvfv1sQDGz5tg3yVCrvPRiM+ORNNazljWp/6aFWnIjoFhyAnT6Xu76Q0x/zAgmIsJiENTzKy4WBtVub7+ap+WbEQjx/GwcrWHjYOTnj8MK7Ivu8NHaezPfF+NH4P3QMXz+rqwgEA3m7RAQ4VK+lc59CWH5GT+RTeTVr8sx14ReWmeFCpVFi6dClWr16NyMhIGBsbo379+vj000/RtWtXqeO9lB+Wr0S16jXg4eGBrxfMx+efTZU6ksHg2JQsPz8fI4cHok7dt1G9Rk1s3bxR6khl6rsfVqBatRqo7OGBb7/+CjM/n6azn5WVNb5dvAT9Bw6GhYWFuv3L+d/gvQ5tELJ/H3bv3I4ePXuXVfQyk5+fj/FjhsPbpy6qVq+Ondu26Oy3acd+mJlp/0f99ZczsXjhfCz/fjFmzJ5X2nHLjI1SiQmTpxfbx8rKCl8sWIxe/QbC/Lnj5uns+ejXrQMOHdiPX/fuROduPUs7bpmyUSox7j/Fjw0A/O+bucjKzMTy9ds0rjKMmzQdnVo2wvIl32LUJxOhUChKM+5rdSMhHRN3X8PVvzRP4H0qWmPe+7UQ1MITZ6OeIFclAACbzus+QRz9rgcA4LfrCRrtrkoFBvq64X5yJsbtuIqnf3/6vudyPBb39MH4VlUxYksE/t68QYlOSIfLoA3IfyGclZkJjs1/H8M61ML3+6/i+r1kmJka4buPmkMIgTbT9uHi3SR1//8Ob4qP3quNT95/C9/suqRuT0rLQudZIbh45xGepOdgz4yO6FBf9wmzIRowZT6c3D3h4FIJoRt+wJ5lC4rs23nYeJ3t274NBgA0e7+vRvvbLTrg7RYdtPrHRl7Gr6sXw7WaFzxr13vl7P9EuZjzIIRAnz59MHbsWKSmpmLYsGHo168fIiMj0a1bNyxZskTqiC+lTdt28PDwkDqGQeLYlOzbb77C5UsR+GHFqn/d7RP6aN2mHSrrcYy4urlh+MjRGoUDAFhaWiLok/EAgJMnjpdGRMktXfwNrl+5hIXfLS/2GNFVOABA5+4FJ8bRUXdKJZ8hc6nohkFDR2oUDgBgYWmJYaMLbg8IP31CimgG4V5MNGQyGVq00Typca3kjpq1aiMrMxMZ6Yb5KXpRTkc90SocAODqX2m4FJcKGzMTeDpY6FjzGRMjGVrXcERuvgpHbj7SWNa+lhOMjeTY+scDdeEAALFPMnH45iO4Ks1Qz035enbmNVOphFbhAADpWbk49GdBEVXNxQYA0LSWM5yU5vjlXIxG4QAAMzf/AQAY3rGWRntGVh6ORDzAk3TNKzXlRa1G78LB5dWLndzsbPweugfGJqZo3LGHXuuc+uUnAECzLn1L6Fl6ysWVhx07dmDHjh1o3rw5Dh48CHNzcwDA3Llz4evri4kTJ6JLly7w9PSUNihRKbt69QrmzpmNyVM/Q+3aPlLHKbdMTEwAAMbG5eJX4EuJvHYV/13wJT75dAq8vGu/0jaOhIYAAGrVerX1DVVOdg62b92I+L8ewEZpi4aN30Htt+rqvX7hcWP0LzxucrKzsWPrRsQ/fACl0hYNGr0Dbx1jU8PLG3dv38TJsENo0+E9dfuDuPu4GXkNXt5vwd7BsSyjl6q8v0+cdZ1AP695VXtYmxnjxJ0kpGTlaSyr61pwcn3hXorWen/EpqCzjzPquFrjwn3t5YZKYWKElnUqQqUSuH4/GQDgbFtwbhadoD1XIeVpDh6nZcOjgjWqOFsjKr58FZil5c9jv+FpWgrqt+4EazuHEvvnZGfhj0N7YWxqisb++hUbpaFc/AbcvXs3AGDatGnqwgEAHB0dMWHCBIwfPx5r1qzBrFmzJEpIVPry8vIwavgQeNXyxqeTpkgdp1zbsH4NAKBNu/YSJ3m98vLy8H9BI1C9Zi2MGT9J7/W2bV6Pe7ExyEhPx+VLF3Hm5HG8VbceRozRfY9ueZWY8BATg0ZotLVs2wH/XbpKrxPebZvXAwBatGpXKvmklJgQj/98MlKjrUWb9lj4vebYTJjyOc6Hn8HHQwLQzr8LPKpUw+OkRBzYvxdulSrju5Ubyjp6qXGyMkX9Sko8zshB9OOnxfbtWMsJAPDb9UStZW5KMzzNyceTzFytZXEpWQAK5ksYMqWFKYLe94FcJoOT0gz+Ddzh7mSFOT9dwJ2/UgEAiX/vi2cFK631bSxMYG9dcCtbDVcli4e/nd63DYD2LUtFuXj0V2Smp6Fhu/dhaWNbismKVy6Kh/j4eABAlSraM9UL244cOcLigf7Vvv5qLi5fikDYybPqT0Dp5a1ZtQIHD/yGlq3aoKP/eyWvUI589+1XuHblEn45eOKljpFtWzbg7Klnt+K0bN0Oi5ethq2tXWnElESf/oPRpJkfatbyhqmpArduXsfir+ci7NABDB/YCzt+PVrsJOjN61Yh7NABNPNrhdbt/csweenrHfAhGjfzQw0vb5gqFLh94zr+981cHDscipGDeuPn/UfUY1PDyxs7QsIQNHwAQn7Zpd6G0tYOvQIGwbNqdal247UyksswqW01mBrLserYvWLnIzhbK1DXzQbxadm4qOPqgoWpEVIy83SsCfVtTJamhn0LqtLSFNP7Pps8n5Obj6nrwrFozxV129kb8UjJyMH7jT3wdhUHREQ9u3UpOKCh+s+2lqZlE9rAPXpwD7cunIGdsytqNfLTa50zL1lslJZyMefByamgoo+KitJaVth28+bNItfPzs5GamqqxhdReXLpUgS+mvclxk34FPXrNyh5BdLpt5D9mDjhE1Su7IEfV6+XOs5rde3KJfxv4XyMChqPOm/Xf6l1t/9yEPcfZ+HSrftYt3UX/noQB/9WTXHt6uVSSlv2xk2ahnea+8HewRFW1tao37AxVm/eiUbvNMOF38Nx9OBvRa57JDQEwVMmwM29Mhb9sLoMU5eNsROnoUmzv8fGyhr1GjbGyk074dukGS6eD0fYoWdjc+nPP9D3/bawtbXHnkOncCX6EcJ+v4be/Qfjy88nI2jYAAn35PWQAZjQqirquNog5FqC1hyGF3Wo5QS5TIaDkYkwwDnPr0VsYjrMP1gFy16r4TXqJ3yx9QJm9vfF1v+0hZG8oLDMyMrD5LXhMDUxQti8Llg9riXmDW6Mo3O7YFiHWoj8+/amkm4Be1Oc2bcNQgg07dwbcnnJp+MJ96Nx+89zcHB1h1fDZmWQsGjlonjo1KkTAGD+/PnIyspStyclJWHRokUAgOTk5CLXnzdvHpRKpfrL3d29NOMSvXYjhwWiatVq+GzGTKmjlFuHDh7AoIDeqFDBGb/8dgguFStKHem1Gv/xcHh4VsX/TZ7xytuwd3BE2w6dsPHnvXjy+BH+M+7j15jQ8MjlcvQO+BAAcP7cGZ19jh05iI+GBMDRqQK27PoNFVz+XcdNUeRyOXoFDAIA/PH32OTm5mLciA8hk8mwbN1PeKtufZhbWMDdwxNTZ85F5+69EPrrXpw5eUzK6P/YuFZV0NbLEYdvPMJ3x7Q/tHyeDED7Wo7IVwmERmrfsgQUXF2wKOLKQmF7xgvvPzBUKpVAbGI6vtl1CbM2/4Fu73hiaHsv9fJ1h2+i2xcHEH4jAV0aV8ZIf2/k5Qu8Fxzy7Pam1KyiNv/GUKlUOBuyAzK5HE076/fEvzO/PCs2pH5UdLm4bSkgIABr1qzB0aNHUadOHfj7+yM3Nxe7d++Gs7MzABT7RJGpU6fi//7v/9R/T01NZQFB5crlSxEAAHsbc53LW7co+BRi67adeL9b97KKVW4cDP0NA/r2hIODI/YdOIwqVapKHem1u3al4PGH1SrqfmpLt44tAQArN2yDf+fiH2/tWskd1WvWQsTF88h8+lTryUP/Jnb2BZMUMzO172kPOxyKUYP7wt7eAVt2H0Blz5Jf8vRv8mxsMgEAd2/dQGxMFDp27qbzmGj6bkvs370dVyIuoum7Lcs06+sgAzC+VRV08K6Ao7ce4dujd0q8kuBbWQknKwXOxyYjsYgnBsWlZKG2izXszE205j24/T3X4UFK+TuhPhQRh7kAWrxVET8eiFS3h168j9CL2u/CWFXZDvn5Kvx5t/grOW+Ca2ePITnhL3g3aQF7F7cS+6vy8xEesgNyIyO9i43SVC6KB2NjY4SEhGD+/PnYvHkzVqxYAaVSiR49emDixImoWbOm+tYmXRQKRbl65jTRiwYPGaqz/dSJE7h9+xY6d+kKRydHVOYTx7QcDP0N/ft8ADs7e+w7cBjVqv077sl+Ub+BgTrbw8+cRNSd2+jQqQvsHRzhXlm/RyEnxD+ETCaD/F/+OOA/L/wOAKjkrjkuYYdDMfLDPrC1tcOW3QfgWbWaFPEk9eLY5OQWnBw/TtJ98lfYbloO/799vnA4disJ3xy+o9d7Fzp4VwAAHNAxUbrQ5QdpqO1ijQbuShx+4RaohpWV6j7lTUW7ggIyL19VQk+gaa0K8HS2Rsgf95D6VHvi+JvmZecuXD1zFClJCXirWRvYOkn/4s5yUTwABQVAcHAwgoODNdrDwsIAAL6+vhKkIiobS5et1Nk+cvgQ3L59CxP/MwWNm7xTxqkMX2HhYGtnh/0HDqN69RpSRyo13/xvmc72CWOGI+rObYwZPwkNGzVRtz95nISE+Hitx7kKIfDtV3OQmBCPd1u2/ld88HLrxnVUcKkIpdJWo/33s6ew8of/wVShgH+Xbur2wsJBqSwoHKr8SwtOoGBsnF0qwuaFsTl/9jRWL/sOpgoFOnYuGJuatXxgbaPEH+fO4MTRQ/Br/eypU/EPH2DTmhUAgHea6Tf501DIAIxvXRUdajnh+O0kLDh8W6/CQWlmjCYetkjJzMXZ6CdF9jsYmYieb7ugX0NXnIl+op4kXdnOHG1rOuJBShb+jDPMx7TW9bRHdEKa1gm/nZUpZg0oOO86cOHZVQZrcxOkvXB1paKdBZZ+7IfcPBVmb/mj9EMbuLQnSbh86jCsbO1R9139ntxW+FSmpl36lGY0vZWb4qEomzZtAgD069dP4iT6W7NqJU6fOgkAuHqlYELimtUrcfxYGADg/W7d0fUNvfWEY0MlWbdmJc6cPgUAuHb1yt9tq3HieMF91l3e74YuXbvj5o1I9O/zAbKzs/GuX0v8vG2r1rY8PDwwYFBgmWU3JA/i7qNjyyao16ARanrVgpOzMx4nJeHc2VO4c+smKji74MsFi6WO+Vrs270Dy5d8i+Z+rVGpcmWYmipwI/IaThw9BLlcji+/+Q5ulSoDAG7fuoGRH/ZBTnY23mnuh707t2ltr1JlD/T+ez5Aeffrnh1Y8f1/0cyvFdzcPWBqqsDNyKs4GXYYcrkcX3z9P7hWKrjNV6FQYNqseZg64WMMDeiO1u39Ua1GLTxKTEDor3uRnpaKQUNHwav2WxLv1cvp7+uGDrWc8DQnH3EpWQhoqH0byZmoJ7ibpHlrW1svR5gYyXHoRrz6fRC6xKVkYdP5OAxu4o6lferg5J3HMDORo1V1BxjJZVgcFmWQb5cGgEFtaiCwrReOXfkL9xLTkZGdh8pOVvBvWAnW5qbYdSYKP5149jLJjzv7IKBFNZyOjEdiSiYqOVihS+PKsFAY46PvT+DPF14eBwDzBjeGw9+PcX2rcsET3uYHNkb630XIN7su4aaBFlen9m7FnUvnAQAP7t4AAJze9xNuXTwLQPdbosN/24n8vFw09u8BY5OSnzyV+jgRV04fhbW9I+o0b/ua9+DVlJviITU1FTY2Nhpt27dvx+rVq9GoUSN88MEHEiV7eadPncTGDes02s6cPqU+IfLw9HxjT5A5NlSSM6dPYfNGzSclnT1zCmfPFBwjlT080aVrd8THP0R2djYAYMfPP+nc1rt+Ld/Y4sHNvTKCJkzCmZMncOTQASQ/eQyFmRmqVK2OcZ9OwfDRY9X3vJd3Td9tids3I3H10p8IP30C2dlZcHSqgC7de2HY6LGo16CRum9ifDxy/j5uftn1s87tNWnm968pHt55tyVu37qBq5f+xLnTJ5GdnQUHpwro3L0Xho4KwtvPjQ0A9BkQCDd3D6xdsQQX//gdYYcOwNzCEt4+ddBnQCA+6Fv+nrbk/PeJq4Wpkc7CAQDi07K1ioeOtQpvWUoo8XtsvfAA8WnZ6F7XBZ19KiBPJXAtPh0bz93HzcSMf7gHpWfX6WjYWJiicc0KeLe2CywUxnicno3T1+OxOew2tp28q9H/bGQ8/Hxc8J5vZdhZmiIpPRsHLtzHwl2XNB7d+rweTT3hUcH6hbZn84s2HL1lsMXDnUvnER6yQ6Pt7qXzuPt3QeFQsZJW8VB4y1Lz9/X70Ds8ZAdU+Xl4p1NPg3lBpUwIYaD1riZvb2+4u7vD29sbZmZmOHfuHMLCwlC1alUcOXIEHh763ccLFBQiSqUS8UkpWgUJUXFUhvrxkAHg4/eKl6rjBVFUoLhPbd90/Lkq2pjtl6SOYLDCDl4pudMbbOFEw/gE35BkZqRhYoe6SEkp+dzYMEoYPfTt2xc7d+7E2bNnkZubiypVqmD69OmYNGkSCwAiIiIiojJQboqHmTNnYubMmVLHICIiIiJ6Y5WLl8QREREREZH0WDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFeWDwQEREREZFejKUOIAUhBAAgLTVV4iRU3qhUQuoIBiufY1OstMxcqSMYrDzBY6co/LkqWl5mhtQRDJbIzZQ6gkHLzEiTOoLBycpIB/DsHLk4MqFPr3+Z+/fvw93dXeoYREREREQG4969e6hUqVKxfd7I4kGlUuHBgwewtraGTCaTNEtqairc3d1x79492NjYSJrF0HBsisfxKRrHpmgcm6JxbIrGsSkax6Z4HJ+iGdLYCCGQlpYGV1dXyOXFz2p4I29bksvlJVZVZc3GxkbyA8dQcWyKx/EpGsemaBybonFsisaxKRrHpngcn6IZytgolUq9+nHCNBERERER6YXFAxERERER6YXFg8QUCgWCg4OhUCikjmJwODbF4/gUjWNTNI5N0Tg2RePYFI1jUzyOT9HK69i8kROmiYiIiIjo5fHKAxERERER6YXFAxERERER6YXFAxERERER6YXFg0R+//13vPfee7Czs4OlpSUaN26MzZs3Sx1Lchs3bsSoUaPg6+sLhUIBmUyGtWvXSh3LIMTFxWHRokXo0KEDKleuDFNTU7i4uKBnz54IDw+XOp6kkpOT8cknn6Bp06ZwcXGBQqGAm5sb2rRpgx07doBTuzQtWLAAMpkMMpkMZ8+elTqOpDw9PdVj8eLXRx99JHU8g7Br1y60b98eDg4OMDc3R5UqVRAQEIB79+5JHU0ya9euLfK4Kfxq27at1DElI4TAzp070bp1a1SsWBEWFhbw8vLCqFGjcPfuXanjSUqlUmHJkiVo0KABLCwsYGNjg5YtW2Lv3r1SR9PbG/mSOKmFhYWhY8eOMDU1Rb9+/aBUKrFz504MGDAA0dHRmDZtmtQRJTN9+nTExMTA0dERFStWRExMjNSRDMZ3332Hr776CtWqVUP79u1RoUIF3Lp1C7t378bu3buxZcsW9OnTR+qYknj06BFWr16Nd955B927d4e9vT0SEhLwyy+/oFevXhgxYgRWrFghdUyDcP36dXz++eewtLRERkaG1HEMglKpxPjx47XafX19yz6MARFC4KOPPsKKFStQrVo19OvXD9bW1njw4AGOHTuGmJgYuLu7Sx1TEvXq1UNwcLDOZdu3b8fVq1fRsWPHMk5lOCZOnIhvv/0WFStWRPfu3WFjY4OIiAj8+OOP2LJlC06fPo233npL6phlTgiBPn36YMeOHahWrRqGDRuG7Oxs7NmzB926dcN3332HoKAgqWOWTFCZys3NFdWqVRMKhUJcuHBB3Z6amip8fHyEsbGxuHnzpoQJpXXw4EERHR0thBBi3rx5AoBYs2aNtKEMxI4dO8Tx48e12o8fPy5MTEyEvb29yMrKkiCZ9PLy8kRubq5We2pqqqhdu7YAIK5cuSJBMsOSl5cnGjVqJBo3biwGDhwoAIgzZ85IHUtSHh4ewsPDQ+oYBmnx4sUCgBgzZozIy8vTWq7rZ+5Nl52dLRwcHISxsbF4+PCh1HEk8ddffwm5XC48PT1FSkqKxrL//ve/AoAYMmSIROmk9fPPPwsAonnz5uLp06fq9sTEROHh4SEUCoWIioqSLqCeeNtSGTty5Aju3LmD/v37o379+up2a2trzJgxA3l5eVizZo2ECaXVrl07eHh4SB3DIH3wwQfw8/PTavfz80Pr1q3x+PFjXL58WYJk0jMyMoKxsfaFVGtra/Wnf7dv3y7rWAbnq6++QkREBFavXg0jIyOp45ABy8zMxKxZs1C1alUsWrRI5/Gi62fuTbdr1y4kJSWhS5cucHZ2ljqOJKKjo6FSqdC8eXPY2NhoLOvcuTMAICEhQYpoktu9ezcAYNq0aTA3N1e3Ozo6YsKECcjOzi4X54D8yS9jYWFhAIAOHTpoLStsO3bsWFlGon8BExMTAPzP/EVZWVk4cuQIZDIZateuLXUcSV25cgWzZs3C9OnT4ePjI3Ucg5KdnY1169YhLi4OdnZ2aNasGd5++22pY0nq4MGDePz4MQIDA5Gfn4+9e/fi5s2bsLW1Rbt27VC9enWpIxqkVatWAQCGDx8ucRLp1KhRA6ampjh16hTS0tJgbW2tXvbrr78CANq0aSNVPEnFx8cDAKpUqaK1rLDtyJEjmDVrVpnmelk80yhjt27dAlDww/UiOzs7ODo6qvsQ6SM2NhaHDh2Ci4sL6tSpI3UcSSUnJ2PRokVQqVRISEjAr7/+inv37iE4OFjnz9ybIi8vD4GBgfD29saUKVOkjmNwHj58iMDAQI02f39/bNiwAY6OjtKEktj58+cBFHwg8fbbb+PGjRvqZXK5HBMmTMA333wjVTyDFBMTg8OHD8PNzQ3+/v5Sx5GMg4MDvvzyS0yaNAne3t7o2rUrrK2tcfnyZRw6dAgjR47E2LFjpY4pCScnJwBAVFQUvL29NZZFRUUBAG7evFnmuV4Wi4cylpKSAqBggp4uNjY2uH//fllGonIsNzcXgwYNQnZ2NhYsWPDG34qSnJys8YmNiYkJvv76a3z66acSppLe3LlzERERgfDwcPVVKiowdOhQtGzZEj4+PlAoFLh27RpmzZqFkJAQdO3aFadOnYJMJpM6ZpkrvK1k4cKFaNCgAc6dOwdvb29cvHgRI0eOxMKFC1GtWjWMHj1a4qSGY82aNVCpVBgyZMgb/7t44sSJcHV1xahRo/DDDz+o25s1a4aBAwe+sb+HOnXqhC1btmD+/Plo06YNzMzMAABJSUlYtGgRgIL/xwwd5zwQlVMqlQpDhw7F8ePHMWLECAwaNEjqSJLz9PSEEAJ5eXmIiorC7Nmz8dlnn6Fnz57Iy8uTOp4kIiIiMGfOHEycOBENGjSQOo7B+fzzz9GyZUs4OjrC2toaTZo0wb59+/Duu+/izJkz6tss3jQqlQoAYGpqit27d6NRo0awsrKCn58ftm/fDrlcjoULF0qc0nCoVCqsWbMGMpkMQ4cOlTqO5ObMmYPAwEBMnToV9+7dQ3p6Ok6ePIm8vDy0bt0aO3fulDqiJAICAtC6dWucOHECderUwdixY/HRRx/Bx8dHPT+kPBSeLB7KWOEVh8IrEC9KTU0t8qoEUSEhBEaMGIGNGzdi4MCBWLZsmdSRDIqRkRE8PT0xZcoUzJkzB7t27cKPP/4odSxJDB48GNWqVcPMmTOljlJuyOVyDBkyBABw6tQpidNIo/D/IV9fX7i6umos8/HxQdWqVXHnzp1y8SlpWTh48CBiY2PRpk0bnfezv0mOHDmCGTNmICgoCNOmTUOlSpVgaWmJ5s2bY9++fTA3N8eECROkjikJY2NjhISEYObMmZDL5VixYgV27tyJbt26Yfv27QCe3dpkyFg8lLHC+651zWt48uQJHj169Ebfm00lU6lUGDZsGFavXo2AgACsXbsWcjl/lItS+CCCwocVvGkiIiIQGRkJMzMzjRdYrVu3DgDQtGlTyGQy9VNAqEDhXIenT59KnEQaXl5eAABbW1udywvbMzMzyyiRYeNE6Wf2798PAGjdurXWMicnJ9SpUwexsbF49OhRWUczCAqFAsHBwbhx4ways7ORkJCA5cuXIy4uDkD5eL8M5zyUsZYtW2LevHkIDQ1Fv379NJaFhoaq+xDpolKpMHz4cKxZswZ9+/bFhg0bysUlTik9ePAAwJv7JKphw4bpbD9+/Dhu3bqFrl27wsnJCZ6enmUbzMAVvrX9TR2XwhO/69evay3Lzc3F7du3YWlpWS4+JS1tSUlJ2LNnD+zt7dGjRw+p40guJycHAJCYmKhzeWG7QqEos0zlwaZNmwBA69zQIEn9ook3TW5urqhatapQKBTi4sWL6vbnXxJ348YN6QIaEL4kTlN+fr4IDAwUAETv3r35gqbnXLx4USQnJ2u1JyUliXr16gkAYsOGDRIkM1yDBw9+418Sd/XqVfHkyROt9hMnTggzMzOhUChETExM2QczEB06dBAAxI8//qjRPnv2bAFADBw4UKJkhqXwxWeffPKJ1FEMwpYtWwQA4ePjo/V7ee3atQKAaNiwoUTppPfii/OEKHh5nFwuF40aNdL5QkZD82Z+FCchY2NjrFy5Eh07doSfnx8CAgJgY2ODnTt3IioqCnPmzEHNmjWljimZlStX4uTJkwCgfuHZypUr1becdO/eHd27d5conbRmz56NtWvXwsrKCjVr1sScOXO0+nTv3h316tUr+3ASW7t2LVauXInWrVvDw8MDlpaWiImJwf79+5Geno6ePXuif//+UsckA7Nt2zYsWLAAbdu2haenJxQKBa5cuYLQ0FDI5XIsW7YMlStXljqmZJYuXYpmzZphxIgR2L17N2rVqoWLFy/iyJEj8PDwwNdffy11RIPAW5Y09e7dG8uXL0dYWBhq1KiBrl27ws7ODhERETh48CAUCoX6yUJvoiZNmsDd3R3e3t4wMzPDuXPnEBYWhqpVq+Lnn38uH3cTSF29vKnCw8OFv7+/UCqVwtzcXPj6+oqNGzdKHUtyhZ+GFvUVHBwsdUTJlDQ2eIOv0pw4cUIEBgaKWrVqCRsbG2FsbCwqVKgg/P39xebNm4VKpZI6osHhlQchwsLCRJ8+fUT16tWFtbW1MDExEZUqVRL9+vUT4eHhUsczCLGxsSIwMFC4uLgIExMT4e7uLsaMGSPi4+OljmYQwsPDBQDRuHFjqaMYlKysLPHVV1+JBg0aCAsLC2FsbCzc3NxE//79xeXLl6WOJ6ng4GBRp04dYW1tLczMzIS3t7eYPn26zisShkomhBBlXrEQEREREVG5w0e0EBERERGRXlg8EBERERGRXlg8EBERERGRXlg8EBERERGRXlg8EBERERGRXlg8EBERERGRXlg8EBERERGRXlg8EBERERGRXlg8EBERERGRXlg8EBH9CwUGBkImkyEwMFBrWatWrSCTyTBz5swyz1Xa/sm+eXp6QiaTYe3atQaT6Z8KCwuDTCaDTCYr8+9NRP9OLB6IiF4wc+ZM9QnX819mZmaoVKkSunbtim3btkEIIXVUg5CcnIyZM2di5syZSE5OljoOERGVImOpAxARGTJnZ2f1n1NSUhAXF4e4uDj88ssvWLt2LXbt2gWFQiFhwpdXuXJleHl5wdHR8bVsLzk5GbNmzQJQcMXD1tb2tWyXiIgMD688EBEV4+HDh+qvjIwMXLlyBe3btwcAhISEYPr06RInfHnr169HZGQkgoKCpI5CRETlDIsHIiI9yeVy+Pj4YO/evahevToAYPny5cjLy5M4GRERUdlg8UBE9JLMzMzQu3dvAEBaWhoiIyMBANHR0er5EdHR0bhz5w5GjhyJKlWqQKFQwNPTU2tbu3fvRvfu3eHq6gpTU1PY2dmhRYsWWLZsGXJzc4vNsWnTJjRv3hzW1tZQKpVo0qQJVqxYUeJcDH0m8F6/fh1jxoxB7dq1YW1tDSsrK3h5eaFfv37YsWMHVCqVeltVqlRRr1elShWNeSKtWrXS2nZ+fj7Wrl2Ljh07wtnZGaampnByckLHjh2xdevWYvPn5+djyZIlaNCgASwtLWFvb49WrVph+/btxe7zPxUbG4vvv/8enTt3Rs2aNWFpaQkrKyvUrl0b48ePR2xsrF7bycnJwfz581G3bl1YWlrCzs4O7du3R0hISInr3rlzB2PHjoW3tzesrKxgYWEBb2/vl/r+RET/mCAiIg3BwcECgCjuV+T333+v7nPq1CkhhBBRUVHqtk2bNgkrKysBQFhYWAhLS0vh4eGhXj8tLU106dJF3R+AsLGxETKZTP33pk2bisePH2t9b5VKJYYMGaLuJ5PJhJ2dnZDL5QKA6Nevnxg8eLAAIAYPHqy1fsuWLQUAERwcrHPf5s+fr94WAGFmZiasra01sj558kQIIUSPHj2Eo6Ojut3R0VE4Ozurv3r06KGx7YcPH4omTZpobEupVGr8vWvXriI7O1srV1ZWlujYsaO6n1wuF7a2tuoxmzx5con7VhwPDw8BQKxZs6bIMXs+8/NjpFQqxYkTJ3Rut3DdqVOnCj8/PwFAGBsbC1tbW41tFpd5xYoVwsTERN1XoVAIc3NzjWMnNDRUa72jR4+WeCwTEb0M/jYhInqBPsXDpEmT1H2uX78uhNAsHqysrESTJk3E77//rl7nxo0b6j93795dABDVq1cXmzdvFqmpqUIIITIzM8WePXtE1apVBQDRvXt3re+9ePFi9fcJCgoSiYmJQgghkpOTxcyZM4VMJlOfmL5s8bB06VKNk/iLFy+qlyUlJYnQ0FDRt29fkZKSom5/fr+joqKKHLPs7GzRqFEjAUA0aNBA7N+/X2RkZAghhEhPTxfr1q0TFSpUEADE+PHjtdafMGGCuliaM2eOOkN8fLwYPXq0RiHyuouHMWPGiPnz54tr166Jp0+fCiGEyM3NFeHh4cLf318AEK6uruplzyscb6VSKRQKhVi2bJnIzMwUQggRGxsrevXqpR6/PXv2aK2/a9cuAUCYmJiIKVOmiOjoaKFSqYRKpRKRkZGid+/e6gIiJiZGY10WD0T0uvG3CRHRC0oqHlJSUoSrq6sAIOzt7UV+fr4QQvMk2sPDQ6Slpelcf9++fQKAcHFxEffv39fZ5969e8LS0lIA0DiBz8zMFPb29gKAGDRokM51p0yZos7xMsXD48eP1VcY+vXrJ1Qqlc7tv0jf4mHJkiUCgPDx8VEXSy86f/68kMlkwtTUVMTHx6vb4+LihLGxsQAgZsyYoXPdgIAAvT7FL0pxxUNx8vLyRN26dQUAsWHDBq3lz1+1WLVqldby/Px80aJFCwFA1K5dW2NZdna2cHNzK3LdQl27dhUAxLhx4zTaWTwQ0evGOQ9ERHpKTk7G4cOH0aZNGzx48AAAMG7cOMjl2r9Kg4KCYGVlpXM7K1euBAAMGjQIbm5uOvtUqlQJrVu3BgAcOHBA3R4aGorHjx8DAD7//HOd606ZMgVmZmZ67tUz27dvR1paGkxMTPDtt9++9heLFe73xx9/DGtra519GjZsCB8fH+Tk5ODo0aMa2fLy8mBubo6JEyfqXFeql94ZGRnB398fAHDy5Mki+7m7u2PIkCFa7XK5XP3UrmvXruHy5cvqZSEhIYiLi4Ozs7POdQt9+OGHADSPFSKi0sD3PBARFaO4E+iBAwfis88+07msefPmRa5XeIK5YsUKrF+/vsh+KSkpAICYmBh12/nz5wEUnIgWPvHpRUqlEg0bNsSpU6eK3LYup0+fBlBwAl+xYsWXWrckaWlpuHTpEgBgxowZmD17dpF9C4sjXfvt6+sLGxsbnevVrFkTbm5uiIuLe12xNZw4cQKrVq3C2bNncf/+fWRkZGj1uX//fpHrF05U16VFixYwNjZGXl4ezp8/jzp16gB4dqw8efKk2H+TnJwcAJpjRkRUGlg8EBEV4/mXxCkUCjg6OqJ+/foYMGCA+sqALhUqVNDZnpubi0ePHgEoKA4KC4TiPH36VP3nhIQEACjyikWhSpUqlbjdFz18+BAA4OHh8dLr6rPtwic0FRYHJXnV/S6N4mHy5MlYsGCB+u9GRkaws7ODqakpACA9PR0ZGRk6C4pCxWVXKBRwcHBAfHy8el8BqK9w5eTkID4+vsScmZmZJfYhIvonWDwQERWj8IT6ZRkZGelsz8/PV/9569at6Nu37ytt/3XfUlTa235+v8+ePYsmTZq80nZKc7+LcvDgQXXh8PHHH2P06NHw9vbW+DeeMWMG5syZU+xjZl8le+G4+fv76/U4VyKi0sY5D0REZcjMzAxKpRIANO5t11fhFY3ibo8B8EqfvhfeFhMdHf3S65bk+Ss4hrbfJdm6dSsAoGPHjvj+++/x1ltvaRWH+hSZxWXPzs5GUlISAM2rVi4uLgBebcyIiEoDiwciojJWOB/i559/Vt/Koy9fX18AwL1793Dnzh2dfVJTU/HHH3+8dK5mzZoBKJhf8Ndff+m93vMTxov65N3Ozg61a9cG8Oxk/GUU7vf58+eRlpams8+tW7dKLC5exb179wAA9evX17lcCIEjR46UuJ1jx44VOT4nTpxQv6m8cF+BZ8dKXFxcsZOxiYjKCosHIqIyNnLkSADAzZs38fXXXxfbNyMjQz0ZFgDat28POzs7AMAXX3yhc50FCxa80r3vvXv3ho2NDfLy8jBhwoQS31Rd6PkJzMnJyUX2K9zvw4cPl1hAvDgvomfPnjA2NkZmZiYWLlyoc53iJmH/E4VXiiIiInQuX7ZsGe7evVvidmJjY7Fu3TqtdpVKhblz5wIAvL291ZOlAeD9999XXxEaN26cxjwQXfSdT0JE9KpYPBARlbFu3bqhR48eAAoeqzp69GjcvHlTvTwnJwfh4eGYPHkyPDw8NCbQmpubY8aMGQCAdevWYfz48erbXVJTU/HFF19g7ty5sLW1felcSqVSfW//Tz/9hB49euDPP/9UL3/y5An279+Pbt26ITU1Vd1ua2urngy8Zs0a9SfoL/roo4/Ucx0GDRqE6dOnqz/VBwomSIeFhSEoKAjVqlXTWNfNzQ0ff/wxgIKiad68eeorEImJiQgKCsLGjRvVJ/qvU+FjWENCQvDFF1+oJ0UnJydj7ty5GDt2LBwcHErcjlKpxOjRo/Hjjz8iKysLQMFVjYCAAPVjab/88kuNdczMzLB06VLIZDJcuHABzZs3x4EDBzQKyqioKCxfvhyNGzfG0qVLX8s+ExEVSdK3TBARGSB93jCti74vSxNCiIyMDNGvXz91fwDC0tJS2NnZCblcrtH+4ovk8vPzxaBBg9TL5XK5sLOzE0ZGRuoXvA0ePPiV3jAthBBz587VyGBubq5+eVzh15MnTzTW+eKLL9TLFAqFcHd3Fx4eHqJv374a/RITE0WbNm00tmVjYyNsbW2FTCZTtxkbG2vlyszMFO3atVP3MTIyEnZ2dur1Jk+eXOK+Faeol8Tl5OQIPz8/9feVyWQa/06dO3cW06dPFwBEy5YttbZbmGnq1Kni3XffVb8t2s7OTmMcpk+fXmS2jRs3CgsLC43xcXBwEAqFQmMbc+bM0ViPL4kjoteNVx6IiCRgYWGBLVu24OjRoxg0aBCqVq0KlUqF9PR0VKhQAW3atMGCBQtw69YtrUd8yuVyrF+/HuvXr8c777wDc3Nz5OXloUGDBli2bBk2b978j7JNnToVERERGDFihPpdEkIIeHl5ISAgADt37tR618K0adOwePFi+Pr6wsTEBPfv30dMTIzWRGJHR0ccOnQIe/bsQa9eveDu7o7s7GxkZmbCzc0NnTp1wpIlS3RO2jYzM0NISAgWL16MevXqwdTUFEII+Pn5Ydu2bZg/f/4/2u+imJiYIDQ0FMHBwahZsyZMTEwghEDjxo3xww8/YO/evUU+Xet5pqamOHz4MObOnQsvLy9kZ2dDqVSibdu22L9/f5G3oQHAgAEDcPv2bUyfPh2+vr6wsrJCcnIyzMzMUK9ePQQFBeHQoUOYPHny69x1IiItMiH0vKmViIiIiIjeaLzyQEREREREemHxQEREREREemHxQEREREREemHxQEREREREemHxQEREREREemHxQEREREREemHxQEREREREemHxQEREREREemHxQEREREREemHxQEREREREemHxQEREREREemHxQEREREREemHxQEREREREemHxQEREREREevl/UKJOdiSgohYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now let's create the confusion matrix plot\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "\n",
    "# Plot the confusion matrix as a heatmap\n",
    "im = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "\n",
    "# Set up the axes\n",
    "ax.set(xticks=np.arange(cm.shape[1]),\n",
    "       yticks=np.arange(cm.shape[0]),\n",
    "       xticklabels=np.arange(10), yticklabels=np.arange(10),\n",
    "       xlabel='Predicted label', ylabel='True label')\n",
    "\n",
    "# Loop over data dimensions and create text annotations\n",
    "for i in range(cm.shape[0]):\n",
    "    for j in range(cm.shape[1]):\n",
    "        ax.text(j, i, format(cm[i, j], 'd'),\n",
    "                ha=\"center\", va=\"center\", fontsize=ticklabel_size,\n",
    "                color=\"white\" if cm[i, j] > cm.max() / 2. else \"black\")\n",
    "\n",
    "# Adjust font sizes\n",
    "ax.set_xlabel('Predicted label', fontsize=label_size)\n",
    "ax.set_ylabel('True label', fontsize=label_size)\n",
    "\n",
    "ax.tick_params(axis='both', which='major', labelsize=ticklabel_size)\n",
    "\n",
    "# Tight layout to ensure everything fits\n",
    "fig.tight_layout()\n",
    "\n",
    "# Save the figure if needed\n",
    "# plt.savefig('Regression_for_classification.png', dpi=300, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e270f67a-0510-4653-a014-1ddd873265c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#使用逻辑回归做分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0ab03e2e-3255-43da-9f6d-218b8735fcb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted classes: [0 1 2 3 4 5 6 7 8 9]\n",
      "Accuracy of logistic regression model: 0.8737\n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.92      0.95      0.93       980\n",
      "           1       0.93      0.96      0.95      1135\n",
      "           2       0.86      0.84      0.85      1032\n",
      "           3       0.80      0.84      0.82      1010\n",
      "           4       0.91      0.91      0.91       982\n",
      "           5       0.77      0.69      0.73       892\n",
      "           6       0.91      0.92      0.92       958\n",
      "           7       0.91      0.89      0.90      1028\n",
      "           8       0.83      0.83      0.83       974\n",
      "           9       0.86      0.89      0.87      1009\n",
      "\n",
      "    accuracy                           0.87     10000\n",
      "   macro avg       0.87      0.87      0.87     10000\n",
      "weighted avg       0.87      0.87      0.87     10000\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Initialize the linear regression model\n",
    "lr_model = LogisticRegression(max_iter=1000)\n",
    "\n",
    "# Train the model\n",
    "lr_model.fit(X_train, y_train)\n",
    "\n",
    "# Make predictions on the test set\n",
    "y_pred = lr_model.predict(X_test)\n",
    "y_pred_proba = lr_model.predict_proba(X_test)\n",
    "\n",
    "# Round predictions to nearest integer for classification\n",
    "y_pred_rounded = np.round(y_pred).astype(int)\n",
    "print(f\"Predicted classes: {np.unique(y_pred_rounded)}\")\n",
    "\n",
    "# Calculate accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f\"Accuracy of logistic regression model: {accuracy:.4f}\")\n",
    "\n",
    "# Calculate and print classification report\n",
    "print(\"\\nClassification Report:\")\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d3a8ebea-4c20-4f7d-ba9e-51685f47fc82",
   "metadata": {},
   "outputs": [],
   "source": [
    "#用混淆矩阵展示逻辑回归的分类结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ed3f30b8-64ea-4373-9e79-f48548f20e63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Now let's create the confusion matrix plot\n",
    "fig, ax = plt.subplots(figsize=(10, 8))\n",
    "\n",
    "# Plot the confusion matrix as a heatmap\n",
    "im = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "\n",
    "# Set up the axes\n",
    "ax.set(xticks=np.arange(cm.shape[1]),\n",
    "       yticks=np.arange(cm.shape[0]),\n",
    "       xticklabels=np.arange(10), yticklabels=np.arange(10),\n",
    "       xlabel='Predicted label', ylabel='True label')\n",
    "\n",
    "# Loop over data dimensions and create text annotations\n",
    "for i in range(cm.shape[0]):\n",
    "    for j in range(cm.shape[1]):\n",
    "        ax.text(j, i, format(cm[i, j], 'd'),\n",
    "                ha=\"center\", va=\"center\", fontsize=ticklabel_size,\n",
    "                color=\"white\" if cm[i, j] > cm.max() / 2. else \"black\")\n",
    "\n",
    "# Adjust font sizes\n",
    "ax.set_xlabel('Predicted label', fontsize=label_size)\n",
    "ax.set_ylabel('True label', fontsize=label_size)\n",
    "\n",
    "ax.tick_params(axis='both', which='major', labelsize=ticklabel_size)\n",
    "\n",
    "# Tight layout to ensure everything fits\n",
    "fig.tight_layout()\n",
    "\n",
    "# Save the figure if needed\n",
    "# plt.savefig('Logicregression_for_classification.png', dpi=300, bbox_inches='tight')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fe939985-c2e1-4d77-b0ed-7ee8688a1e63",
   "metadata": {},
   "outputs": [],
   "source": [
    "#选择三个随机样本来展示输出的形式，尤其是Softmax前后的概率分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9d7211cb-b148-4fd2-86f4-db850bf47249",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted classes:  [1 2 6]\n",
      "Probabilities:\n",
      "Example 1: 2.22%, 71.31%, 1.81%, 1.64%, 10.57%, 3.32%, 0.13%, 3.07%, 3.91%, 2.03%\n",
      "Example 2: 2.71%, 0.23%, 89.79%, 0.93%, 0.00%, 5.31%, 0.87%, 0.01%, 0.15%, 0.00%\n",
      "Example 3: 0.12%, 0.03%, 0.29%, 0.18%, 1.02%, 0.33%, 96.80%, 0.00%, 1.18%, 0.04%\n"
     ]
    }
   ],
   "source": [
    "# Select an example of character 1 randomly\n",
    "# Select an example of 1 randomly\n",
    "char_1_indices = np.where(y_test == 1)[0]\n",
    "random_char_1_index = np.random.choice(char_1_indices)\n",
    "random_char_1 = X_test[random_char_1_index]\n",
    "\n",
    "# Select an example of 2 randomly\n",
    "char_2_indices = np.where(y_test == 2)[0]\n",
    "random_char_2_index = np.random.choice(char_2_indices)\n",
    "random_char_2 = X_test[random_char_2_index]\n",
    "\n",
    "# Select an example of 6 randomly\n",
    "char_6_indices = np.where(y_test == 6)[0]\n",
    "random_char_6_index = np.random.choice(char_6_indices)\n",
    "random_char_6 = X_test[random_char_6_index]\n",
    "\n",
    "# Get predictions and probabilities for these examples\n",
    "examples = [random_char_1, random_char_2, random_char_6]\n",
    "example_predictions = lr_model.predict(examples)\n",
    "example_probabilities = lr_model.predict_proba(examples)\n",
    "\n",
    "print(\"Predicted classes: \", example_predictions)\n",
    "\n",
    "# Display probabilities in percentage style, one line per example\n",
    "print(\"Probabilities:\")\n",
    "for i, probs in enumerate(example_probabilities):\n",
    "    prob_strings = [f\"{prob:.2%}\" for _, prob in enumerate(probs)]\n",
    "    print(f\"Example {i+1}: {', '.join(prob_strings)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "76f32f39-75dc-497e-a6ab-ffab07496dba",
   "metadata": {},
   "outputs": [],
   "source": [
    "#常用的二分类模型——支持向量机"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a9585fab-e005-46fc-9091-c1b7602e934c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#构建二分类数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6949d131-8021-4c04-bdff-db61015b8786",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract features and labels from trainset\n",
    "x_train = []\n",
    "y_train = []\n",
    "for image, label in trainset:\n",
    "    x_train.append(image.numpy())\n",
    "    y_train.append(1 if label == 1 else 0)  # Set label to 1 for character 1, 0 otherwise\n",
    "\n",
    "x_train = np.array(x_train)\n",
    "y_train = np.array(y_train)\n",
    "\n",
    "# Extract features and labels from trainset\n",
    "x_test = []\n",
    "y_test = []\n",
    "for image, label in testset:\n",
    "    x_test.append(image.numpy())\n",
    "    y_test.append(1 if label == 1 else 0)  # Set label to 1 for character 1, 0 otherwise\n",
    "\n",
    "x_test = np.array(x_test)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d861ea1f-5366-4d94-bd76-94218515a7ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#支持向量机的基本逻辑"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "47e8ae02-ff64-40a1-b572-fb71841e146f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 186.36 seconds\n"
     ]
    }
   ],
   "source": [
    "# Define SVM classifier\n",
    "mdl_svm = svm.SVC(kernel='linear', probability=True)\n",
    "\n",
    "# Train model\n",
    "start_time = time.time()\n",
    "mdl_svm.fit(x_train, y_train)\n",
    "end_time = time.time()\n",
    "\n",
    "print(f'Training time: {end_time - start_time:.2f} seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6cdb6d6c-1c73-4944-988b-07b05e0b5892",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.9755, Recall: 0.9454, Accuracy: 0.9911, F1-Score: 0.9602\n"
     ]
    }
   ],
   "source": [
    "# Make predictions and evaluate the model\n",
    "y_pred_svm = mdl_svm.predict(x_test)\n",
    "y_proba_svm = mdl_svm.predict_proba(x_test) # Output ratio\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred_svm)\n",
    "precision = precision_score(y_test, y_pred_svm)\n",
    "recall = recall_score(y_test, y_pred_svm)\n",
    "f1 = f1_score(y_test, y_pred_svm)\n",
    "\n",
    "print(f'Precision: {precision:.4f}, Recall: {recall:.4f}, Accuracy: {accuracy:.4f}, F1-Score: {f1:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "45ba3155-20a6-4ba6-a5be-de5c4d791452",
   "metadata": {},
   "outputs": [],
   "source": [
    "#常用的二分类模型——决策树和随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d4ee4144-a54a-471f-92d8-fe10a2ccdac3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 6.18 seconds\n",
      "Training time: 35.83 seconds\n"
     ]
    }
   ],
   "source": [
    "# Define DecisionTree classifier\n",
    "mdl_dt = tree.DecisionTreeClassifier()\n",
    "\n",
    "# Train model\n",
    "start_time = time.time()\n",
    "mdl_dt.fit(x_train, y_train)\n",
    "end_time = time.time()\n",
    "\n",
    "print(f'Training time: {end_time - start_time:.2f} seconds')\n",
    "\n",
    "# Define Random Forest classifier\n",
    "mdl_rf = RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "# Train model\n",
    "start_time = time.time()\n",
    "mdl_rf.fit(x_train, y_train)\n",
    "end_time = time.time()\n",
    "\n",
    "print(f'Training time: {end_time - start_time:.2f} seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ee202af2-8d01-4b80-8b5a-b1f7b78ec0aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.9159, Recall: 0.9304, Accuracy: 0.9824, F1-Score: 0.9231\n",
      "Precision: 0.9907, Recall: 0.9374, Accuracy: 0.9919, F1-Score: 0.9633\n"
     ]
    }
   ],
   "source": [
    "y_pred_dt = mdl_dt.predict(x_test)\n",
    "y_proba_dt = mdl_dt.predict_proba(x_test) # Output ratio\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred_dt)\n",
    "precision = precision_score(y_test, y_pred_dt)\n",
    "recall = recall_score(y_test, y_pred_dt)\n",
    "f1 = f1_score(y_test, y_pred_dt)\n",
    "\n",
    "print(f'Precision: {precision:.4f}, Recall: {recall:.4f}, Accuracy: {accuracy:.4f}, F1-Score: {f1:.4f}')\n",
    "\n",
    "y_pred_rf = mdl_rf.predict(x_test)\n",
    "y_proba_rf = mdl_rf.predict_proba(x_test) # Output ratio\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred_rf)\n",
    "precision = precision_score(y_test, y_pred_rf)\n",
    "recall = recall_score(y_test, y_pred_rf)\n",
    "f1 = f1_score(y_test, y_pred_rf)\n",
    "\n",
    "print(f'Precision: {precision:.4f}, Recall: {recall:.4f}, Accuracy: {accuracy:.4f}, F1-Score: {f1:.4f}')"
   ]
  }
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